Journals >Advanced Photonics Nexus
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3 Issue (s), 37 Article (s)
Vol. 4, Iss.6—Nov.1, 2025 • pp: 066001-066017 Spec. pp:
Vol. 4, Iss.5—Sep.1, 2025 • pp: 054001-059901 Spec. pp:
Vol. 5, Iss.1—Jan.1, 2026 • pp: 016001-016003 Spec. pp:
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Vol. 4, Iss.6-Nov..1,2025
Research Articles
Fluorescence microscopy image denoising via a wavelet-enhanced transformer based on DnCNN networkShuhao Shen, Mingxuan Cao, Weikai Tan, E Du, and Xueli Chen
Fluorescence microscopy is indispensable in life science research, yet denoising remains challenging due to varied biological samples and imaging conditions. We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer–convolutional neural network (CNN) architecture. Wavelet decomposition separates high- and low-frequency components for targeted noise reduction; the CNN branch restores local details, whereas the transformer branch captures global context; and an adaptive loss balances quantitative fidelity with perceptual quality. On the fluorescence microscopy denoising benchmark, our method surpasses leading CNN- and transformer-based approaches, improving peak signal-to-noise ratio by 2.34% and 0.88% and structural similarity index measure by 0.53% and 1.07%, respectively. This framework offers enhanced generalization and practical gains for fluorescence image denoising.Fluorescence microscopy is indispensable in life science research, yet denoising remains challenging due to varied biological samples and imaging conditions. We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer–convolutional neural network (CNN) architecture. Wavelet decomposition separates high- and low-frequency components for targeted noise reduction; the CNN branch restores local details, whereas the transformer branch captures global context; and an adaptive loss balances quantitative fidelity with perceptual quality. On the fluorescence microscopy denoising benchmark, our method surpasses leading CNN- and transformer-based approaches, improving peak signal-to-noise ratio by 2.34% and 0.88% and structural similarity index measure by 0.53% and 1.07%, respectively. This framework offers enhanced generalization and practical gains for fluorescence image denoising.
Advanced Photonics Nexus
- Publication Date: Oct. 15, 2025
- Vol. 4, Issue 6, 066001 (2025)
Research Articles
Microscopic structured light 3D imaging via a scattering lensWenjing Zhao, Wei Chang, Youtao Wang, Aiping Zhai, Fei Liu, and Dong Wang
Transforming a scattering medium into a lens for imaging very simple binary objects is possible; however, it remains challenging to image complex grayscale objects, let alone measure 3D continuous distribution objects. Here, we propose and demonstrate the use of a ground glass diffuser as a scattering lens for imaging complex grayscale fringes, and we employ it to achieve microscopic structured light 3D imaging (MSL3DI). The ubiquitous property of the speckle patterns permits the exploitation of the scattering medium as an ultra-thin scattering lens with a variable focal length and a flexible working distance for microscale object measurement. The method provides a light, flexible, and cost-effective imaging device as an alternative to microscope objectives or telecentric lenses in conventional MSL3DI systems. We experimentally demonstrate that employing a scattering lens allows us to achieve relatively good phase information and robust 3D imaging from depth measurements, yielding measurement accuracy only marginally lower than that of a telecentric lens, typically within approximately 10 μm. Furthermore, the scattering lens demonstrates robust performance even when the imaging distance exceeds the typical working distance of a telecentric lens. The proposed method facilitates the application of scattering imaging techniques, providing a more flexible solution for MSL3DI.Transforming a scattering medium into a lens for imaging very simple binary objects is possible; however, it remains challenging to image complex grayscale objects, let alone measure 3D continuous distribution objects. Here, we propose and demonstrate the use of a ground glass diffuser as a scattering lens for imaging complex grayscale fringes, and we employ it to achieve microscopic structured light 3D imaging (MSL3DI). The ubiquitous property of the speckle patterns permits the exploitation of the scattering medium as an ultra-thin scattering lens with a variable focal length and a flexible working distance for microscale object measurement. The method provides a light, flexible, and cost-effective imaging device as an alternative to microscope objectives or telecentric lenses in conventional MSL3DI systems. We experimentally demonstrate that employing a scattering lens allows us to achieve relatively good phase information and robust 3D imaging from depth measurements, yielding measurement accuracy only marginally lower than that of a telecentric lens, typically within approximately 10 μm. Furthermore, the scattering lens demonstrates robust performance even when the imaging distance exceeds the typical working distance of a telecentric lens. The proposed method facilitates the application of scattering imaging techniques, providing a more flexible solution for MSL3DI.
Advanced Photonics Nexus
- Publication Date: Oct. 15, 2025
- Vol. 4, Issue 6, 066002 (2025)
Research Articles
Dual-split-ring resonant metasurface with both quasi-BIC and dipole modes for terahertz trace sensing of hyaluronic acidJiyue Chen, Jining Li, Kai Chen, Xiang Yang, Degang Xu, and Jianquan Yao
The resonance generated by different mechanisms naturally has different characteristics in sensing, and these differences increase the potential for specific detection. We designed a metasurface with both a quasi-bound state in continuum (QBIC) resonance and dipole resonance by conducting physical analyses such as electric field, current distribution, and multiple expansions on a dual-split-ring resonance with dipole resonance and a variant structure with symmetry breaking. On the other hand, the edge length of the slit was extended through a tilted split design, which further enhanced the QBIC resonance signal of the metasurface. In the sensing experiment of hyaluronic acid (HA), the limit of detection (LOD) obtained through frequency shift was 0.958 pmol / μL, whereas the LOD obtained through the change in transmittance was 0.02 pmol / μL. Our research findings contribute to the design of multiple resonant metasurfaces with different resonance modes, promoting further development in metasurface research and biosensing.The resonance generated by different mechanisms naturally has different characteristics in sensing, and these differences increase the potential for specific detection. We designed a metasurface with both a quasi-bound state in continuum (QBIC) resonance and dipole resonance by conducting physical analyses such as electric field, current distribution, and multiple expansions on a dual-split-ring resonance with dipole resonance and a variant structure with symmetry breaking. On the other hand, the edge length of the slit was extended through a tilted split design, which further enhanced the QBIC resonance signal of the metasurface. In the sensing experiment of hyaluronic acid (HA), the limit of detection (LOD) obtained through frequency shift was 0.958 pmol / μL, whereas the LOD obtained through the change in transmittance was 0.02 pmol / μL. Our research findings contribute to the design of multiple resonant metasurfaces with different resonance modes, promoting further development in metasurface research and biosensing.
Advanced Photonics Nexus
- Publication Date: Oct. 16, 2025
- Vol. 4, Issue 6, 066003 (2025)
Research Articles
Customizing the field of view for imaging through scattering mediaDajiang Lu, Juncheng Chen, Yibin Tian, Jiapeng Cai, Zaoxin Chen, Xiang Peng, and Wenqi He
Noninvasive speckle autocorrelation is a promising technique for single-shot optical imaging through scattering media. However, it fails to image multiple distinct targets within an object space through scattering media because it is constrained by the tiny effective range of the optical memory effect. We present a method for multi-object single-shot imaging through scattering media that incorporates deep learning into the speckle autocorrelation technique, wherein the field of view (FOV) is customized by recovered autocorrelation sidelobes, and a conventional phase-retrieval algorithm is applied to a complete set of expected speckle autocorrelations to identify multiple target objects and their relative positions. Experiments verify the feasibility of customizing the FOV for imaging through scattering media. Image reconstruction results show that the proposed approach produces superior image quality compared to existing methods. We also demonstrate its generalization capability across different object types and unknown scattering media.Noninvasive speckle autocorrelation is a promising technique for single-shot optical imaging through scattering media. However, it fails to image multiple distinct targets within an object space through scattering media because it is constrained by the tiny effective range of the optical memory effect. We present a method for multi-object single-shot imaging through scattering media that incorporates deep learning into the speckle autocorrelation technique, wherein the field of view (FOV) is customized by recovered autocorrelation sidelobes, and a conventional phase-retrieval algorithm is applied to a complete set of expected speckle autocorrelations to identify multiple target objects and their relative positions. Experiments verify the feasibility of customizing the FOV for imaging through scattering media. Image reconstruction results show that the proposed approach produces superior image quality compared to existing methods. We also demonstrate its generalization capability across different object types and unknown scattering media.
Advanced Photonics Nexus
- Publication Date: Oct. 22, 2025
- Vol. 4, Issue 6, 066004 (2025)
Research Articles
Index-adapting cladding light stripper for high-power thulium fiber lasersTilman Lühder, Till Walbaum and Thomas Schreiber
Cladding light strippers (CLSs) are essential components for high-power monolithic fiber laser systems. Because they allow for bending of the fiber, which leads to an excellent stripping efficiency of light with a low ray angle, refractive index-based CLSs have an advantage over the commonly used alternative approaches. However, conventional high-index CLSs overheat at relatively low input power as the maximum temperature, located in a hot-spot, increases linearly with the input power. This applies particularly to CLSs in thulium-based fiber systems, where very low power can already lead to extreme heat generation due to the high cladding material absorption around 2 μm. Here, we investigate materials with a highly negative thermo-optical coefficient combined with a refractive index closely above glass to distribute the stripped power and heat uniformly along an increased fiber length. Analyzing multiple CLS geometries for fiber diameters of 125 and 400 μm, we show record-high maximum input powers for single-material CLSs of 21.8 W for the signal (2039 nm) and 675 W for the pump wavelength (793 nm). Transmitting excess light instead of overheating, this wavelength-adaptable self-protecting CLS concept is fast to apply onsite in the lab and reaches stripping efficiencies of >40 dB in the bent version.Cladding light strippers (CLSs) are essential components for high-power monolithic fiber laser systems. Because they allow for bending of the fiber, which leads to an excellent stripping efficiency of light with a low ray angle, refractive index-based CLSs have an advantage over the commonly used alternative approaches. However, conventional high-index CLSs overheat at relatively low input power as the maximum temperature, located in a hot-spot, increases linearly with the input power. This applies particularly to CLSs in thulium-based fiber systems, where very low power can already lead to extreme heat generation due to the high cladding material absorption around 2 μm. Here, we investigate materials with a highly negative thermo-optical coefficient combined with a refractive index closely above glass to distribute the stripped power and heat uniformly along an increased fiber length. Analyzing multiple CLS geometries for fiber diameters of 125 and 400 μm, we show record-high maximum input powers for single-material CLSs of 21.8 W for the signal (2039 nm) and 675 W for the pump wavelength (793 nm). Transmitting excess light instead of overheating, this wavelength-adaptable self-protecting CLS concept is fast to apply onsite in the lab and reaches stripping efficiencies of >40 dB in the bent version.
Advanced Photonics Nexus
- Publication Date: Oct. 31, 2025
- Vol. 4, Issue 6, 066005 (2025)
Research Articles
Sub-diffraction-limited focusing laser pulses at ultrahigh intensity via near-critical-density hollow plasma fibersTianqi Xu, Zhuo Pan, Ying Gao, Yulan Liang, Shirui Xu, Qingfan Wu, Tan Song, Yujia Zhang, Haoran Chen, Qihang Han, Chenghao Hua, Ziyang Peng, Ke Chen, Xuan Liu, and Wenjun Ma
Ultra-intense electromagnetic fields exceeding 1023 W / cm2 are enabling breakthroughs in compact laser-driven particle accelerators and revealing new quantum electrodynamics (QED) phenomena. However, conventional laser-focusing methods face considerable engineering challenges and require substantial costs. Focusing schemes utilizing plasma optics can produce sub-micrometer focus spots beyond the diffraction limit and substantially enhance the peak intensity; however, owing to significant energy dissipation, they may fail to simultaneously increase the laser fluence. To address these challenges, we propose a focusing scheme employing a near-critical-density hollow plasma fiber (HPF) that utilizes graded refractive index dynamics to boost both laser peak intensity and fluence at the same time. Three-dimensional particle-in-cell simulations demonstrate the HPF’s capability to focus a 4.5-μm-diameter Gaussian beam to a sub-diffraction-limited 0.6-μm-diameter spot. The peak intensity and laser fluence can be enhanced by factors of 22 and 10, respectively, marking a substantial improvement over existing plasma-based focusing schemes. Furthermore, the proposed scheme exhibits wide-range parameter adaptation and high robustness, making it suitable for direct implementation in PW-class ultra-intense laser experiments.Ultra-intense electromagnetic fields exceeding 1023 W / cm2 are enabling breakthroughs in compact laser-driven particle accelerators and revealing new quantum electrodynamics (QED) phenomena. However, conventional laser-focusing methods face considerable engineering challenges and require substantial costs. Focusing schemes utilizing plasma optics can produce sub-micrometer focus spots beyond the diffraction limit and substantially enhance the peak intensity; however, owing to significant energy dissipation, they may fail to simultaneously increase the laser fluence. To address these challenges, we propose a focusing scheme employing a near-critical-density hollow plasma fiber (HPF) that utilizes graded refractive index dynamics to boost both laser peak intensity and fluence at the same time. Three-dimensional particle-in-cell simulations demonstrate the HPF’s capability to focus a 4.5-μm-diameter Gaussian beam to a sub-diffraction-limited 0.6-μm-diameter spot. The peak intensity and laser fluence can be enhanced by factors of 22 and 10, respectively, marking a substantial improvement over existing plasma-based focusing schemes. Furthermore, the proposed scheme exhibits wide-range parameter adaptation and high robustness, making it suitable for direct implementation in PW-class ultra-intense laser experiments.
Advanced Photonics Nexus
- Publication Date: Nov. 07, 2025
- Vol. 4, Issue 6, 066007 (2025)
Research Articles
Research on the application of MobileNetV1 neural network model for small-sample OAM mode recognitionYanyu Lu, Dahai Yang, Xikun Chen, Zhihao Xu, Wu Zhang, and Xianyou Wang
Deep learning (DL) models have demonstrated significant value in computational perception, super-resolution imaging, ultra-precision measurement, and photonic device design. In optical communication signal recognition, DL models can achieve fast and accurate identification. However, in high-capacity optical communication systems represented by orbital angular momentum (OAM) beams, neural networks often suffer from excessive parameter sizes and demand large training datasets. To address these challenges, we report a lightweight MobileNetV1 model optimized with efficient channel attention to perform OAM mode recognition after transmission through free space and underwater tank environments. Experimental results show that in simulated small-sample classification tasks with five samples per class, the proposed model achieves an accuracy of 99.67% even under moderate turbulence conditions, outperforming four other DL models. In addition, for experimental datasets collected from both atmospheric turbulence and underwater environments, the model consistently achieves recognition accuracies exceeding 90%, demonstrating strong generalization ability and a 77% reduction in parameter count compared to traditional convolutional neural network (CNN)-based DL models. We provide a new approach for deploying lightweight DL algorithms on resource-constrained embedded optical signal detection devices.Deep learning (DL) models have demonstrated significant value in computational perception, super-resolution imaging, ultra-precision measurement, and photonic device design. In optical communication signal recognition, DL models can achieve fast and accurate identification. However, in high-capacity optical communication systems represented by orbital angular momentum (OAM) beams, neural networks often suffer from excessive parameter sizes and demand large training datasets. To address these challenges, we report a lightweight MobileNetV1 model optimized with efficient channel attention to perform OAM mode recognition after transmission through free space and underwater tank environments. Experimental results show that in simulated small-sample classification tasks with five samples per class, the proposed model achieves an accuracy of 99.67% even under moderate turbulence conditions, outperforming four other DL models. In addition, for experimental datasets collected from both atmospheric turbulence and underwater environments, the model consistently achieves recognition accuracies exceeding 90%, demonstrating strong generalization ability and a 77% reduction in parameter count compared to traditional convolutional neural network (CNN)-based DL models. We provide a new approach for deploying lightweight DL algorithms on resource-constrained embedded optical signal detection devices.
Advanced Photonics Nexus
- Publication Date: Nov. 08, 2025
- Vol. 4, Issue 6, 066008 (2025)
Research Articles
Spatially confined tumor phototherapy enabled by GHz laser thermal accumulation dynamicsZhi Chen, Changle Meng, Huiling Lin, Dror Fixler, and Han Zhang
Achieving precise tumor ablation without damaging surrounding healthy tissue remains a significant challenge in cancer therapy, particularly for deep-seated or irregularly shaped tumors. Traditional laser-based approaches, although minimally invasive, are often limited by insufficient tissue penetration, uncontrolled thermal damage, and narrow therapeutic windows. We introduce GHz high-repetition-rate pulsed lasers as a transformative modality for tumor ablation. This approach capitalizes on the thermal accumulation effect of GHz pulse trains, in which the pulse interval is significantly shorter than the thermal relaxation time of biological tissue. Such a regime enables efficient and localized heat deposition in tumor regions. By precisely tuning the repetition frequency, pulse duration, and energy density, we establish a dynamic “ablation-cooling” cycle: rapid energy delivery followed by transient inter-pulse cooling. This thermal modulation ensures sharply confined ablation zones with reduced collateral damage. Our systematic investigation of laser-tissue interaction parameters demonstrates that GHz lasers offer superior spatial selectivity, minimized off-target injury, and enhanced treatment safety, presenting a compelling rationale for clinical translation of this paradigm in precision photothermal oncology.Achieving precise tumor ablation without damaging surrounding healthy tissue remains a significant challenge in cancer therapy, particularly for deep-seated or irregularly shaped tumors. Traditional laser-based approaches, although minimally invasive, are often limited by insufficient tissue penetration, uncontrolled thermal damage, and narrow therapeutic windows. We introduce GHz high-repetition-rate pulsed lasers as a transformative modality for tumor ablation. This approach capitalizes on the thermal accumulation effect of GHz pulse trains, in which the pulse interval is significantly shorter than the thermal relaxation time of biological tissue. Such a regime enables efficient and localized heat deposition in tumor regions. By precisely tuning the repetition frequency, pulse duration, and energy density, we establish a dynamic “ablation-cooling” cycle: rapid energy delivery followed by transient inter-pulse cooling. This thermal modulation ensures sharply confined ablation zones with reduced collateral damage. Our systematic investigation of laser-tissue interaction parameters demonstrates that GHz lasers offer superior spatial selectivity, minimized off-target injury, and enhanced treatment safety, presenting a compelling rationale for clinical translation of this paradigm in precision photothermal oncology.
Advanced Photonics Nexus
- Publication Date: Nov. 12, 2025
- Vol. 4, Issue 6, 066009 (2025)
Research Articles
Demonstration of the MICRO solar magnetograph using silicon nitride photonics and interferometric imagingHumphry Chen, Shelbe Timothy, Neal Hurlburt, Gopal Vasudevan, Lawrence Shing, Tony Kowalczyk, and Simon Avery
We demonstrate a silicon nitride photonics-based imaging system that can perform one-dimensional interferometric imaging around the 1550-nm wavelength. The magnetograph using interferometric and computational imaging for remote observations (MICRO) design uses silicon nitride on a Si platform to replace the bulky free-space optics of traditional magnetograph imaging systems with nanofabricated structures of a fraction of the size. The photonic integrated circuit (PIC) uses an array of lenslets that couple light into four input waveguides with spacing arranged along a Golomb ruler, where each aperture pair formed has a unique length. Each aperture is mixed with a 13-dBm reference laser and separated inside a 2 × 4 optical hybrid to generate in-phase and quadrature-phase signals to be detected in balanced detectors at the output of the PIC. We use a field programmable gate array (FPGA) board to digitize and process the measurements. The FPGAs and PIC are combined to reduce the overall size, weight, and power of the system, paving the way for a compact imaging system. We demonstrate a PIC-based imager design and experimental testbed for spectrometry applications.We demonstrate a silicon nitride photonics-based imaging system that can perform one-dimensional interferometric imaging around the 1550-nm wavelength. The magnetograph using interferometric and computational imaging for remote observations (MICRO) design uses silicon nitride on a Si platform to replace the bulky free-space optics of traditional magnetograph imaging systems with nanofabricated structures of a fraction of the size. The photonic integrated circuit (PIC) uses an array of lenslets that couple light into four input waveguides with spacing arranged along a Golomb ruler, where each aperture pair formed has a unique length. Each aperture is mixed with a 13-dBm reference laser and separated inside a 2 × 4 optical hybrid to generate in-phase and quadrature-phase signals to be detected in balanced detectors at the output of the PIC. We use a field programmable gate array (FPGA) board to digitize and process the measurements. The FPGAs and PIC are combined to reduce the overall size, weight, and power of the system, paving the way for a compact imaging system. We demonstrate a PIC-based imager design and experimental testbed for spectrometry applications.
Advanced Photonics Nexus
- Publication Date: Nov. 19, 2025
- Vol. 4, Issue 6, 066010 (2025)
Research Articles
Comprehensive experimental evaluation of temporal contrast enhancement techniques applied to the petawatt-class J-KAREN-P laser systemHiromitsu Kiriyama, Akito Sagisaka, Yasuhiro Miyasaka, Akira Kon, Mamiko Nishiuchi, Alexander S. Pirozhkov, Yuji Fukuda, Koichi Ogura, Kotaro Kondo, Nobuhiko Nakanii, Yuji Mashiba, Nicholas P. Dover, Liu Chang, Stefan Bock, Tim Ziegler, Thomas Püschel, Karl Zeil, Ulrich Schramm, Il Woo Choi, and Chang Hee Nam
We present a systematic experimental investigation of temporal contrast enhancement techniques for petawatt (PW)-class Ti:sapphire lasers utilizing a double chirped-pulse amplification (CPA) architecture. Particular attention is given to pre-pulses induced by post-pulses originating in the first CPA stage. One conventional and two advanced pulse-cleaning strategies are quantitatively evaluated: (i) a saturable absorber (SA), (ii) a femtosecond optical parametric amplifier (OPA) employing the idler pulse in a two-stage configuration, and (iii) sum-frequency generation (SFG) combining the signal and idler pulses from the OPA. All techniques are implemented and evaluated using the J-KAREN-P laser system with an output energy of about 20 J. To the best of our knowledge, this is the first report to directly and systematically compare the contrast of pre-pulses originating from the first CPA stage under identical experimental conditions in a high-energy PW-class laser facility. The results offer crucial insights into contrast optimization for future high-field applications.We present a systematic experimental investigation of temporal contrast enhancement techniques for petawatt (PW)-class Ti:sapphire lasers utilizing a double chirped-pulse amplification (CPA) architecture. Particular attention is given to pre-pulses induced by post-pulses originating in the first CPA stage. One conventional and two advanced pulse-cleaning strategies are quantitatively evaluated: (i) a saturable absorber (SA), (ii) a femtosecond optical parametric amplifier (OPA) employing the idler pulse in a two-stage configuration, and (iii) sum-frequency generation (SFG) combining the signal and idler pulses from the OPA. All techniques are implemented and evaluated using the J-KAREN-P laser system with an output energy of about 20 J. To the best of our knowledge, this is the first report to directly and systematically compare the contrast of pre-pulses originating from the first CPA stage under identical experimental conditions in a high-energy PW-class laser facility. The results offer crucial insights into contrast optimization for future high-field applications.
Advanced Photonics Nexus
- Publication Date: Nov. 19, 2025
- Vol. 4, Issue 6, 066011 (2025)
Research Articles
High-fidelity and compact topology architecture for large-scale reconfigurable linear optical networks | Editors' PickShuai Lin, Jinjie Zeng, Shuqing Lin, Siyuan Yu, and Yanfeng Zhang
Reconfigurable linear optical networks based on Mach-Zehnder interferometer (MZI) offer significant potential in optical information processing, particularly in emerging photonic quantum computing systems. However, device losses and calibration errors accumulate as network complexity grows, posing challenges in performing precise mapping of matrix operations. Existing architectures, such as Diamond and Bokun, introduce MZI redundancy into Reck and Clements architectures to improve reliability, which increases complexity and differential path losses that limit scalability. We propose a compact topology architecture that achieves 100% fidelity by employing a symmetrical MZI to decouple optical loss from power ratio and introducing extra MZIs to enforce uniform loss distributions. This multi-level optimization enables direct monitoring pathways while supporting precise calibration, and it approaches theoretical fidelity in practical deployments with direct implications for scalable and fault-tolerant photonic computing systems.Reconfigurable linear optical networks based on Mach-Zehnder interferometer (MZI) offer significant potential in optical information processing, particularly in emerging photonic quantum computing systems. However, device losses and calibration errors accumulate as network complexity grows, posing challenges in performing precise mapping of matrix operations. Existing architectures, such as Diamond and Bokun, introduce MZI redundancy into Reck and Clements architectures to improve reliability, which increases complexity and differential path losses that limit scalability. We propose a compact topology architecture that achieves 100% fidelity by employing a symmetrical MZI to decouple optical loss from power ratio and introducing extra MZIs to enforce uniform loss distributions. This multi-level optimization enables direct monitoring pathways while supporting precise calibration, and it approaches theoretical fidelity in practical deployments with direct implications for scalable and fault-tolerant photonic computing systems.
Advanced Photonics Nexus
- Publication Date: Nov. 19, 2025
- Vol. 4, Issue 6, 066012 (2025)
Research Articles
Compressive incoherent digital holography for high-fidelity 3D imagingNing Xu, Dalong Qi, Long Cheng, Zhen Pan, Wenzhang Lin, Chengyu Zhou, Hongmei Ma, Yunhua Yao, Yuecheng Shen, Lianzhong Deng, Zhenrong Sun, and Shian Zhang
Incoherent digital holography has attracted significant attention due to its advantages in three-dimensional (3D) imaging under low spatial coherence conditions, such as easy access to light sources and reduced speckle noise. However, interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity. Furthermore, existing deconvolution- and deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization. To address these challenges, we propose a compressive incoherent digital holography (CIDH) approach for 3D imaging. In CIDH, a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computer-generated holography method for dual-channel forward data acquisition. For scene reconstruction, a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval. The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity. In simulations, crosstalk was effectively suppressed across 10 depth layers. In experiments, successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object, resulting in significantly improved reconstruction accuracy. The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.Incoherent digital holography has attracted significant attention due to its advantages in three-dimensional (3D) imaging under low spatial coherence conditions, such as easy access to light sources and reduced speckle noise. However, interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity. Furthermore, existing deconvolution- and deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization. To address these challenges, we propose a compressive incoherent digital holography (CIDH) approach for 3D imaging. In CIDH, a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computer-generated holography method for dual-channel forward data acquisition. For scene reconstruction, a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval. The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity. In simulations, crosstalk was effectively suppressed across 10 depth layers. In experiments, successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object, resulting in significantly improved reconstruction accuracy. The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.
Advanced Photonics Nexus
- Publication Date: Nov. 20, 2025
- Vol. 4, Issue 6, 066013 (2025)
Research Articles
Implicit neural representation based on optoelectronic periodic nonlinear activationJiawei Gu, Yulong Huang, Zijie Chen, Mu Ku Chen, and Zihan Geng
Implicit neural representation (INR) networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neural networks, enabling lossless compression and efficient reconstruction of details in a compact form. However, an optical-assisted INR network has yet to be demonstrated. INR networks require high nonlinearity, whereas implementing analog nonlinear activation in photonic neural networks is a challenge. Inspired by the inherent physical properties of modulators, we propose an optoelectronic nonlinear activation and implement it on the image reconstruction task. Simulations and experiments demonstrate that the proposed optoelectronic periodic neural network can represent images and perform image reconstruction with excellent results. This approach empowers complex image reconstruction with high-frequency details and reduces the amount of required hardware. Our method enables the development of compact, efficient optoelectronic neural networks, utilizing repeatable modular units for scalable and practical high-performance computing. It can enable scene generation and compression in biomedicine, autonomous driving, and augmented reality/virtual reality.Implicit neural representation (INR) networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neural networks, enabling lossless compression and efficient reconstruction of details in a compact form. However, an optical-assisted INR network has yet to be demonstrated. INR networks require high nonlinearity, whereas implementing analog nonlinear activation in photonic neural networks is a challenge. Inspired by the inherent physical properties of modulators, we propose an optoelectronic nonlinear activation and implement it on the image reconstruction task. Simulations and experiments demonstrate that the proposed optoelectronic periodic neural network can represent images and perform image reconstruction with excellent results. This approach empowers complex image reconstruction with high-frequency details and reduces the amount of required hardware. Our method enables the development of compact, efficient optoelectronic neural networks, utilizing repeatable modular units for scalable and practical high-performance computing. It can enable scene generation and compression in biomedicine, autonomous driving, and augmented reality/virtual reality.
Advanced Photonics Nexus
- Publication Date: Nov. 20, 2025
- Vol. 4, Issue 6, 066014 (2025)
Research Articles
Elliptical vectorial metrics for physically plausible polarization information analysisRunchen Zhang, Xuke Qiu, Yifei Ma, Zimo Zhao, An Aloysius Wang, Jinge Guo, Ji Qin, Steve J. Elston, Stephen M. Morris, and Chao He
The Mueller matrix polar decomposition method decomposes a Mueller matrix into a diattenuator, a retarder, and a depolarizer. Among these elements, the retarder, which plays a key role in medical and material characterization, is usually modelled as a circular retarder followed by a linear retarder. However, this model may not accurately reflect the actual structure of the retarder in certain cases as many practical retarders do not have a layered structure or consist of multiple (unknown) layers. Misinterpretation, therefore, may occur when the actual structure differs from the model. Here, we circumvent this limitation by proposing to use an elliptical retarder parameter set that includes the axis orientation angle φ, the degree of ellipticity χ, and the elliptical retardance ρ. By working with this set of parameters, an overall characterization of any retarder is provided, encompassing its full optical response without making any assumptions about the structure of the material. In this study, experiments were carried out on liquid crystalline samples to validate the feasibility of our approach, demonstrating that the elliptical retarder parameter set adopted provides a useful tool for a broader range of applications in optical material analysis.The Mueller matrix polar decomposition method decomposes a Mueller matrix into a diattenuator, a retarder, and a depolarizer. Among these elements, the retarder, which plays a key role in medical and material characterization, is usually modelled as a circular retarder followed by a linear retarder. However, this model may not accurately reflect the actual structure of the retarder in certain cases as many practical retarders do not have a layered structure or consist of multiple (unknown) layers. Misinterpretation, therefore, may occur when the actual structure differs from the model. Here, we circumvent this limitation by proposing to use an elliptical retarder parameter set that includes the axis orientation angle φ, the degree of ellipticity χ, and the elliptical retardance ρ. By working with this set of parameters, an overall characterization of any retarder is provided, encompassing its full optical response without making any assumptions about the structure of the material. In this study, experiments were carried out on liquid crystalline samples to validate the feasibility of our approach, demonstrating that the elliptical retarder parameter set adopted provides a useful tool for a broader range of applications in optical material analysis.
Advanced Photonics Nexus
- Publication Date: Dec. 01, 2025
- Vol. 4, Issue 6, 066015 (2025)
Research Articles
Hybrid physics-informed and data-driven mode solver for optical fiber design | Editors' PickXiao Luo, Min Zhang, Zhuo Wang, Xiaotian Jiang, Yuchen Song, and Danshi Wang
An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers. By incorporating the governing Helmholtz equation into training, the working mechanism of the proposed operator adheres to the physics essence of fiber analysis. The training of the mode-solving operator adopts a hybrid physics-informed and data-driven approach, providing the advantages of strong physical consistency, enhanced prediction accuracy, and reduced data dependency in comparison with purely data-driven methods. Benefiting from the improvements in network input–output mapping formulation, the proposed operator offers broader applicability to different fiber types and greater flexibility for property optimization. Combined with the particle swarm optimization and refractive index optimization, the operator demonstrates its capacity for the inverse design of multi-step-index fibers (MSIFs) and graded-index fibers (GRIFs). For MSIFs, to ensure a low mode crosstalk for short-distance transmission systems, optimized refractive index profiles (RIPs) of both three-ring and four-ring structures are obtained from large structure parameter search spaces. For GRIFs, to ensure a low receiving complexity for long-haul transmission systems, optimized RIP with low root mean square mode group delay is obtained through point-wise fine-tuning. Moreover, the operator is capable of analyzing the effect of dopant diffusion in manufacturing.An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers. By incorporating the governing Helmholtz equation into training, the working mechanism of the proposed operator adheres to the physics essence of fiber analysis. The training of the mode-solving operator adopts a hybrid physics-informed and data-driven approach, providing the advantages of strong physical consistency, enhanced prediction accuracy, and reduced data dependency in comparison with purely data-driven methods. Benefiting from the improvements in network input–output mapping formulation, the proposed operator offers broader applicability to different fiber types and greater flexibility for property optimization. Combined with the particle swarm optimization and refractive index optimization, the operator demonstrates its capacity for the inverse design of multi-step-index fibers (MSIFs) and graded-index fibers (GRIFs). For MSIFs, to ensure a low mode crosstalk for short-distance transmission systems, optimized refractive index profiles (RIPs) of both three-ring and four-ring structures are obtained from large structure parameter search spaces. For GRIFs, to ensure a low receiving complexity for long-haul transmission systems, optimized RIP with low root mean square mode group delay is obtained through point-wise fine-tuning. Moreover, the operator is capable of analyzing the effect of dopant diffusion in manufacturing.
Advanced Photonics Nexus
- Publication Date: Nov. 20, 2025
- Vol. 4, Issue 6, 066016 (2025)
Research Articles
Physics-informed meta neural representation for high-fidelity, aberration-corrected, sparse-view Fourier ptychographic tomographyMinglu Sun, Fenghe Zhong, Shiqi Mao, Ying Liu, Zihao Zhang, Dongyu Li, Binbing Liu, and Peng Fei
Label-free 3D tomography has attracted growing attention in biological imaging due to its inherent resistance to phototoxicity and concise system configuration. Among existing techniques, Fourier ptychographic tomography (FPT) stands out for high-resolution refractive index (RI) reconstruction from noninterferometric measurements, avoiding coherent noise and phase instability—key limitations of optical diffraction tomography. However, conventional FPT suffers from significant artifacts and high computational demands, especially for multiscattering samples and long-term observation. Here, we introduce physics-informed aberration-corrected meta neural representation (PAMR), an advanced self-supervised framework that integrates neural representation with physics prior, meta-learning optimization, and adaptive aberration correction. Simulations and experiments show that PAMR produces high-fidelity 3D reconstructions with reduced artifacts and strong optical section ability, achieving 137 and 550 nm resolution for lateral and axial, respectively. Moreover, PAMR exhibits superior sparse-view robustness, sustaining high-quality with 75% view reduction. Through the meta-learning strategy, the reconstruction speed of dynamic volumes could be increased by 10 times. Applications include 3D RI imaging of multiscattering C. elegans and long-term 3D observation of HeLa cells, showing detailed organelle structures and interactions. As a generalizable approach combining computational efficiency with physical accuracy, PAMR provides an advanced algorithm for label-free 3D microscopy, with broad applicability across biomedical research.Label-free 3D tomography has attracted growing attention in biological imaging due to its inherent resistance to phototoxicity and concise system configuration. Among existing techniques, Fourier ptychographic tomography (FPT) stands out for high-resolution refractive index (RI) reconstruction from noninterferometric measurements, avoiding coherent noise and phase instability—key limitations of optical diffraction tomography. However, conventional FPT suffers from significant artifacts and high computational demands, especially for multiscattering samples and long-term observation. Here, we introduce physics-informed aberration-corrected meta neural representation (PAMR), an advanced self-supervised framework that integrates neural representation with physics prior, meta-learning optimization, and adaptive aberration correction. Simulations and experiments show that PAMR produces high-fidelity 3D reconstructions with reduced artifacts and strong optical section ability, achieving 137 and 550 nm resolution for lateral and axial, respectively. Moreover, PAMR exhibits superior sparse-view robustness, sustaining high-quality with 75% view reduction. Through the meta-learning strategy, the reconstruction speed of dynamic volumes could be increased by 10 times. Applications include 3D RI imaging of multiscattering C. elegans and long-term 3D observation of HeLa cells, showing detailed organelle structures and interactions. As a generalizable approach combining computational efficiency with physical accuracy, PAMR provides an advanced algorithm for label-free 3D microscopy, with broad applicability across biomedical research.
Advanced Photonics Nexus
- Publication Date: Dec. 09, 2025
- Vol. 4, Issue 6, 066017 (2025)
Vol. 4, Iss.5-Sep..1,2025
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About the Cover: Advanced Photonics Nexus Volume 4 Issue 5The article provides information about the image on the cover of Advanced Photonics Nexus, Volume 4 Issue 5.The article provides information about the image on the cover of Advanced Photonics Nexus, Volume 4 Issue 5.
Advanced Photonics Nexus
- Publication Date: Oct. 31, 2025
- Vol. 4, Issue 5, 059901 (2025)
Reviews
Recent developments of micro-scaled LED-based technologies and mechanisms in the fields of healthcareHe Huang, Longting He, Shirui Cai, Yuxuan Liu, Xiaokuo He, Xinxin Zheng, Shouqiang Lai, Tingzhu Wu, and Zhong Chen
Micro-scaled light-emitting diode (LED) technology has emerged as a transformative tool in biomedical applications, offering innovative solutions across disease surveillance, treatment, and symptom rehabilitation. In disease surveillance, micro-scaled LEDs enable real-time, noninvasive monitoring of physiological parameters through wearable devices, such as skin-like health patches and wireless pulse oximeters; these systems leverage the miniaturization, low power consumption, and high precision of micro-scaled LEDs to track heart rate, blood oxygenation, and neural activity with exceptional accuracy. For disease treatment, micro-scaled LEDs play a pivotal role in optogenetic stimulation and phototherapy. By delivering specific light wavelengths, they enable precise cellular control for cardiac regeneration, neural modulation, and targeted cancer therapies, such as photodynamic therapy with reduced invasiveness. In addition, wireless micro-scaled LED systems facilitate localized and sustained treatments for conditions such as diabetic retinopathy. For symptom rehabilitation, micro-scaled LED-based devices enhance functional and aesthetic outcomes, exemplified by optical cochlear implants for high-resolution hearing restoration and flexible photostimulation patches for hair regrowth. The performance of micro-scale LEDs also brings new possibilities to the field of brain–computer interface. These applications highlight the versatility of micro-scaled LEDs in improving patient quality of life through minimally invasive, energy-efficient, and biocompatible solutions. Although there are still challenges in long-term stability and scalability, the integration of micro-scaled LEDs with advanced biomedical technologies promises to redefine personalized healthcare and therapeutic efficacy.Micro-scaled light-emitting diode (LED) technology has emerged as a transformative tool in biomedical applications, offering innovative solutions across disease surveillance, treatment, and symptom rehabilitation. In disease surveillance, micro-scaled LEDs enable real-time, noninvasive monitoring of physiological parameters through wearable devices, such as skin-like health patches and wireless pulse oximeters; these systems leverage the miniaturization, low power consumption, and high precision of micro-scaled LEDs to track heart rate, blood oxygenation, and neural activity with exceptional accuracy. For disease treatment, micro-scaled LEDs play a pivotal role in optogenetic stimulation and phototherapy. By delivering specific light wavelengths, they enable precise cellular control for cardiac regeneration, neural modulation, and targeted cancer therapies, such as photodynamic therapy with reduced invasiveness. In addition, wireless micro-scaled LED systems facilitate localized and sustained treatments for conditions such as diabetic retinopathy. For symptom rehabilitation, micro-scaled LED-based devices enhance functional and aesthetic outcomes, exemplified by optical cochlear implants for high-resolution hearing restoration and flexible photostimulation patches for hair regrowth. The performance of micro-scale LEDs also brings new possibilities to the field of brain–computer interface. These applications highlight the versatility of micro-scaled LEDs in improving patient quality of life through minimally invasive, energy-efficient, and biocompatible solutions. Although there are still challenges in long-term stability and scalability, the integration of micro-scaled LEDs with advanced biomedical technologies promises to redefine personalized healthcare and therapeutic efficacy.
Advanced Photonics Nexus
- Publication Date: Sep. 03, 2025
- Vol. 4, Issue 5, 054001 (2025)
Research Articles
Visible light red, green, and blue multiplexer by sputter-deposited thin-film lithium niobateAtsushi Shimura, Jiro Yoshinari, Hiroki Hara, Hiroshi Take, Tetsuya Mino, Shigeru Mieda, Takashi Kikukawa, Katsumi Kawasaki, Yasuhiro Takagi, and Hideaki Fukuzawa
Thin-film lithium niobate (TFLN) possesses great potential because it enables high-speed modulation by voltage, which allows higher resolution and lower power consumption for laser beam scanning than direct laser modulation. To achieve these functions, a red, green, and blue (RGB) multiplexer using TFLN is required as an important building block for photonic integrated circuits. We fabricated an RGB multiplexer using TFLN and experimentally confirmed its operation. Three different laser lights of red (λ = 638 nm), green (λ = 520 nm), and blue (λ = 473 nm) were successfully coupled as a single laser beam by an RGB multiplexer composed of multimode interferometers. Furthermore, the TFLN was fabricated by sputter deposition, whereas conventionally, it is fabricated via bulk-lithium niobate adhesion to the substrate. The sputter-deposited TFLN is advantageous for large-volume mass production.Thin-film lithium niobate (TFLN) possesses great potential because it enables high-speed modulation by voltage, which allows higher resolution and lower power consumption for laser beam scanning than direct laser modulation. To achieve these functions, a red, green, and blue (RGB) multiplexer using TFLN is required as an important building block for photonic integrated circuits. We fabricated an RGB multiplexer using TFLN and experimentally confirmed its operation. Three different laser lights of red (λ = 638 nm), green (λ = 520 nm), and blue (λ = 473 nm) were successfully coupled as a single laser beam by an RGB multiplexer composed of multimode interferometers. Furthermore, the TFLN was fabricated by sputter deposition, whereas conventionally, it is fabricated via bulk-lithium niobate adhesion to the substrate. The sputter-deposited TFLN is advantageous for large-volume mass production.
Advanced Photonics Nexus
- Publication Date: Jul. 25, 2025
- Vol. 4, Issue 5, 056001 (2025)
Research Articles
Snapshot multispectral imaging through defocusing and a Fourier imager networkXilin Yang, Michael John Fanous, Hanlong Chen, Ryan Lee, Paloma Casteleiro Costa, Yuhang Li, Luzhe Huang, Yijie Zhang, and Aydogan Ozcan
Multispectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information; this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25% for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others.Multispectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information; this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25% for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others.
Advanced Photonics Nexus
- Publication Date: Aug. 04, 2025
- Vol. 4, Issue 5, 056002 (2025)
Research Articles
Wide-field mid-infrared cavity-enhanced upconversion imagingYue Song, Jia'nan Fang, Wen Zhang, Yijing Li, Ben Sun, Zhiwei Jia, Kun Huang, and Heping Zeng
Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poling crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle of up to 28.5 deg over a spectral coverage of 2.5 to 5 μm. Moreover, a thermal locking approach is used to stabilize the cavity at high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO2 gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications, including environment monitoring, gas leakage inspection, and medical diagnostics.Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poling crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle of up to 28.5 deg over a spectral coverage of 2.5 to 5 μm. Moreover, a thermal locking approach is used to stabilize the cavity at high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO2 gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications, including environment monitoring, gas leakage inspection, and medical diagnostics.
Advanced Photonics Nexus
- Publication Date: Aug. 07, 2025
- Vol. 4, Issue 5, 056003 (2025)
Research Articles
Converting a conventional camera to a super-camera: directional atmospheric scattering modeling for passive imaging in intense real-world scattering scenarios | Editors' PickYihui Fan, Xin Jin, Shun Zou, and Haiyang Yu
Passive imaging through intense atmospheric scattering is a critical yet formidable challenge in optical imaging, with profound implications across various applications. Conventional cameras struggle under severe scattering conditions, fundamentally limiting their effectiveness. We propose a groundbreaking directional atmospheric scattering model that revolutionizes passive imaging capabilities, converting a conventional camera to a super-camera. The model precisely characterizes directional photon propagation through scattering media, transforming this historically ill-posed problem into a well-posed solution, based on which a 4D spatial-angular scattering reconstruction method is proposed, which leverages both ballistic photons and directionally resolved scattered light, without relying on any scene-specific priors, to achieve unprecedented passive imaging performance enabling color imaging through over 12 transport mean free paths at distances up to 1.76 km. Our system recovers targets contributing as little as 0.00016% of the total detected signal, enhancing a standard camera’s signal recovery capacity by nearly 200×. To validate our approach, we introduce the first-ever real-world multiperspective scattering dataset, providing a critical benchmark for future research. We mark a paradigm shift in passive imaging, offering transformative potential for real-world applications under extreme atmospheric scattering conditions.Passive imaging through intense atmospheric scattering is a critical yet formidable challenge in optical imaging, with profound implications across various applications. Conventional cameras struggle under severe scattering conditions, fundamentally limiting their effectiveness. We propose a groundbreaking directional atmospheric scattering model that revolutionizes passive imaging capabilities, converting a conventional camera to a super-camera. The model precisely characterizes directional photon propagation through scattering media, transforming this historically ill-posed problem into a well-posed solution, based on which a 4D spatial-angular scattering reconstruction method is proposed, which leverages both ballistic photons and directionally resolved scattered light, without relying on any scene-specific priors, to achieve unprecedented passive imaging performance enabling color imaging through over 12 transport mean free paths at distances up to 1.76 km. Our system recovers targets contributing as little as 0.00016% of the total detected signal, enhancing a standard camera’s signal recovery capacity by nearly 200×. To validate our approach, we introduce the first-ever real-world multiperspective scattering dataset, providing a critical benchmark for future research. We mark a paradigm shift in passive imaging, offering transformative potential for real-world applications under extreme atmospheric scattering conditions.
Advanced Photonics Nexus
- Publication Date: Aug. 18, 2025
- Vol. 4, Issue 5, 056004 (2025)
Research Articles
Chip-scale wavelength-domain optical Ising machineXinyu Liu, Wenkai Zhang, Wenguang Xu, Hailong Zhou, Ming Li, Jianji Dong, and Xinliang Zhang
Ising problems are critical for a wide range of applications. Solving these problems on a photonic platform takes advantage of the unique properties of photons, such as high speed, low power consumption, and large bandwidth. Recently, there has been growing interest in using photonic platforms to accelerate the optimization of Ising models, paving the way for the development of ultrafast hardware in machine learning. However, these proposed systems face challenges in simultaneously achieving high spin scalability, encoding flexibility, and low system complexity. We propose a wavelength-domain optical Ising machine that utilizes optical signals at different wavelengths to represent distinct Ising spins for Ising simulation. We design and experimentally validate a chip-scale Ising machine capable of solving classical non-deterministic polynomial-time problems. The proposed Ising machine supports 32 spins and features 2 distinct coupling encoding schemes. Furthermore, we demonstrate the feasibility of scaling the system to 256 spins. This approach verifies the viability of performing Ising simulations in the wavelength dimension, offering substantial advantages in scalability. These advancements lay the groundwork for future large-scale expansion and practical applications in cloud computing.Ising problems are critical for a wide range of applications. Solving these problems on a photonic platform takes advantage of the unique properties of photons, such as high speed, low power consumption, and large bandwidth. Recently, there has been growing interest in using photonic platforms to accelerate the optimization of Ising models, paving the way for the development of ultrafast hardware in machine learning. However, these proposed systems face challenges in simultaneously achieving high spin scalability, encoding flexibility, and low system complexity. We propose a wavelength-domain optical Ising machine that utilizes optical signals at different wavelengths to represent distinct Ising spins for Ising simulation. We design and experimentally validate a chip-scale Ising machine capable of solving classical non-deterministic polynomial-time problems. The proposed Ising machine supports 32 spins and features 2 distinct coupling encoding schemes. Furthermore, we demonstrate the feasibility of scaling the system to 256 spins. This approach verifies the viability of performing Ising simulations in the wavelength dimension, offering substantial advantages in scalability. These advancements lay the groundwork for future large-scale expansion and practical applications in cloud computing.
Advanced Photonics Nexus
- Publication Date: Aug. 18, 2025
- Vol. 4, Issue 5, 056005 (2025)
Research Articles
Purcell-enhanced picosecond emission in semiconducting 4H/6H-SiC monocrystalline nanowire forest microcavityXueli Sun, Qin Ling, Ruonan Miao, Huaxin Wu, and Jiyang Fan
Silicon carbide (core third-generation wide-bandgap semiconductor) nanowires have superior characteristics and vital engineering potential in microelectric and photonic devices operating in harsh high-temperature and strong-irradiation environments. Herein, the dense monocrystalline forest-like 4H- and 6H-SiC nanowires (intrinsically bound as a single crystal) are fabricated using the top–down peeling method. They exhibit broadband light emissions spanning the red–green–blue spectral region. The naturally formed microcavity encapsulating the SiC nanowires yields discrete and multimodal emission lines; the luminescence lifetimes decrease to the order of picoseconds owing to improved photon density of states in the microcavity by the quantum electrodynamic Purcell effect. The measured Purcell factor of 8.35 agrees well with the theoretical value of 8.6. The low-temperature luminescence and work functions show significant dependence on the nanowire polytype. The luminescence exhibits peculiar staircase-function enhancement when the temperature is elevated to 200 K, owing to suppression of nonradiative transition channels.Silicon carbide (core third-generation wide-bandgap semiconductor) nanowires have superior characteristics and vital engineering potential in microelectric and photonic devices operating in harsh high-temperature and strong-irradiation environments. Herein, the dense monocrystalline forest-like 4H- and 6H-SiC nanowires (intrinsically bound as a single crystal) are fabricated using the top–down peeling method. They exhibit broadband light emissions spanning the red–green–blue spectral region. The naturally formed microcavity encapsulating the SiC nanowires yields discrete and multimodal emission lines; the luminescence lifetimes decrease to the order of picoseconds owing to improved photon density of states in the microcavity by the quantum electrodynamic Purcell effect. The measured Purcell factor of 8.35 agrees well with the theoretical value of 8.6. The low-temperature luminescence and work functions show significant dependence on the nanowire polytype. The luminescence exhibits peculiar staircase-function enhancement when the temperature is elevated to 200 K, owing to suppression of nonradiative transition channels.
Advanced Photonics Nexus
- Publication Date: Aug. 20, 2025
- Vol. 4, Issue 5, 056006 (2025)
Research Articles
Learning from better simulation: creating highly realistic synthetic data for deep learning in scattering mediaBozhen Zhou, Zhitao Hao, Zhenbo Ren, Edmund Y. Lam, Jianshe Ma, and Ping Su
Obtaining the ground truth for imaging through the scattering objects is always a challenging task. Furthermore, the scattering process caused by complex media is too intricate to be accurately modeled by either traditional physical models or neural networks. To address this issue, we present a learning from better simulation (LBS) method. Utilizing the physical information from a single experimentally captured image through an optimization-based approach, the LBS method bypasses the multiple-scattering process and directly creates highly realistic synthetic data. The data can then be used to train downstream models. As a proof of concept, we train a simple U-Net solely on the synthetic data and demonstrate that it generalizes well to experimental data without requiring any manual labeling. 3D holographic particle field monitoring is chosen as the testing bed, and simulation and experimental results are presented to demonstrate the effectiveness and robustness of the proposed technique for imaging of complex scattering media. The proposed method lays the groundwork for reliable particle field imaging in high concentration. The concept of utilizing realistic synthetic data for training can be significantly beneficial in various deep learning-based imaging tasks, especially those involving complex scattering media.Obtaining the ground truth for imaging through the scattering objects is always a challenging task. Furthermore, the scattering process caused by complex media is too intricate to be accurately modeled by either traditional physical models or neural networks. To address this issue, we present a learning from better simulation (LBS) method. Utilizing the physical information from a single experimentally captured image through an optimization-based approach, the LBS method bypasses the multiple-scattering process and directly creates highly realistic synthetic data. The data can then be used to train downstream models. As a proof of concept, we train a simple U-Net solely on the synthetic data and demonstrate that it generalizes well to experimental data without requiring any manual labeling. 3D holographic particle field monitoring is chosen as the testing bed, and simulation and experimental results are presented to demonstrate the effectiveness and robustness of the proposed technique for imaging of complex scattering media. The proposed method lays the groundwork for reliable particle field imaging in high concentration. The concept of utilizing realistic synthetic data for training can be significantly beneficial in various deep learning-based imaging tasks, especially those involving complex scattering media.
Advanced Photonics Nexus
- Publication Date: Aug. 26, 2025
- Vol. 4, Issue 5, 056007 (2025)
Research Articles
Circular interleaving scan OCT enhances motion-contrast for 360 deg large-field iris angiographyGongpu Lan, Delie Kong, Qun Shi, Zhipeng Wei, Jingjiang Xu, Yanping Huang, Jia Qin, Lin An, Michael D. Twa, and Xunbin Wei
In vivo imaging of human iris vasculature remains a persistent challenge, limiting our understanding of its relationship with ocular disease pathogenesis. Conventional raster scan optical coherence tomography angiography (OCTA) suffers from angular-dependent contrast (including blind spots), limited field of view, and prolonged imaging time—challenges that restrict its clinical utility. We introduce a circular interleaving scan OCTA method that overcomes these barriers by enabling 360 deg high-contrast iris angiography with consistent spatiotemporal sampling and optimized motion contrast. The circular scan design enables direction-optimized sampling: we configured circumferential sampling density to approximately twice the radial density, enhancing detection of radially oriented iris vasculature. A Cartesian–polar coordinate transformation was implemented for eye-motion compensation, vessel realignment, and vasculature reconstruction. Compared with raster scan OCTA, our circular scan protocol demonstrates 1.55× higher efficiency in iris vascular imaging, featuring a superior duty cycle (99.95% versus 82.00%) and eliminating redundant data acquisition from rectangular field corners (27.3% of the circular area). This method improves vessel density measurement by 39.0% and vessel count quantification by 25.2% relative to raster scans. By eliminating angular-dependent blind spots, our method significantly enhances vascular quantification reliability, paving the way to a better understanding of ocular diseases and holding promising potential for future clinical applications.In vivo imaging of human iris vasculature remains a persistent challenge, limiting our understanding of its relationship with ocular disease pathogenesis. Conventional raster scan optical coherence tomography angiography (OCTA) suffers from angular-dependent contrast (including blind spots), limited field of view, and prolonged imaging time—challenges that restrict its clinical utility. We introduce a circular interleaving scan OCTA method that overcomes these barriers by enabling 360 deg high-contrast iris angiography with consistent spatiotemporal sampling and optimized motion contrast. The circular scan design enables direction-optimized sampling: we configured circumferential sampling density to approximately twice the radial density, enhancing detection of radially oriented iris vasculature. A Cartesian–polar coordinate transformation was implemented for eye-motion compensation, vessel realignment, and vasculature reconstruction. Compared with raster scan OCTA, our circular scan protocol demonstrates 1.55× higher efficiency in iris vascular imaging, featuring a superior duty cycle (99.95% versus 82.00%) and eliminating redundant data acquisition from rectangular field corners (27.3% of the circular area). This method improves vessel density measurement by 39.0% and vessel count quantification by 25.2% relative to raster scans. By eliminating angular-dependent blind spots, our method significantly enhances vascular quantification reliability, paving the way to a better understanding of ocular diseases and holding promising potential for future clinical applications.
Advanced Photonics Nexus
- Publication Date: Sep. 17, 2025
- Vol. 4, Issue 5, 056008 (2025)
Research Articles
Generation of sub-three-cycle pulses via double-stage all-fiber nonlinear compression from a thulium-doped fiber laserYan Wu, Yu Cai, Guoqing Zhou, Jintao Fan, Youjian Song, Shiying Cao, and Minglie Hu
We demonstrate few-cycle pulse generation based on double-stage all-fiber nonlinear pulse compression from a thulium-doped fiber laser at a repetition rate of ∼199.74 MHz. The homemade laser provides an average power of 130 mW, serving as the seed for subsequent amplification. After amplification, significant spectral broadening to an octave-spanning bandwidth (1.2 to 2.4 μm) is attained through self-phase modulation-dominated nonlinear effects in an ultrahigh numerical aperture fiber and a highly nonlinear fiber. Followed by a two-stage nonlinear compressor, the system directly delivers near transform-limited pulses with a pulse duration of 19.8 fs (2.9 cycles at a central wavelength of 2000 nm) and a pulse energy of 3.37 nJ. To the best of our knowledge, this result is the shortest pulse duration directly generated from a thulium-doped fiber laser. This robust and simplified all-fiber system provides a promising route toward practical mid-infrared frequency comb generation and mid-infrared spectroscopy.We demonstrate few-cycle pulse generation based on double-stage all-fiber nonlinear pulse compression from a thulium-doped fiber laser at a repetition rate of ∼199.74 MHz. The homemade laser provides an average power of 130 mW, serving as the seed for subsequent amplification. After amplification, significant spectral broadening to an octave-spanning bandwidth (1.2 to 2.4 μm) is attained through self-phase modulation-dominated nonlinear effects in an ultrahigh numerical aperture fiber and a highly nonlinear fiber. Followed by a two-stage nonlinear compressor, the system directly delivers near transform-limited pulses with a pulse duration of 19.8 fs (2.9 cycles at a central wavelength of 2000 nm) and a pulse energy of 3.37 nJ. To the best of our knowledge, this result is the shortest pulse duration directly generated from a thulium-doped fiber laser. This robust and simplified all-fiber system provides a promising route toward practical mid-infrared frequency comb generation and mid-infrared spectroscopy.
Advanced Photonics Nexus
- Publication Date: Sep. 17, 2025
- Vol. 4, Issue 5, 056009 (2025)
Research Articles
Homodyne coherent inter-satellite communications with IM/DD comparable DSP | Editors' PickJunda Chen, Kun Li, Tianjin Mei, Mingming Zhang, Zihe Hu, Jiajun Zhou, Chen Liu, Ming Tang, and Peter A. Andrekson
The rapid development of low earth orbit (LEO) satellite communication networks imposes stringent bandwidth, cost, and power consumption requirements. Conventional intradyne detection (ID) architectures struggle with high Doppler frequency shifts (DFSs), necessitating excessive sampling rates and complex digital signal processing (DSP), resulting in elevated power consumption. This study proposes an inter-satellite polarization division multiplexing self-homodyne detection (PDM-SHD) architecture that compensates for DFSs in the optical domain by co-transmitting a polarization-orthogonal carrier light. The proposed architecture could achieve Nyquist sampling and half-quantization noise, leading to a 53.9% reduction in analog-to-digital converter power consumption under 40 Gbps 16-QAM transmission with a 16 dB signal-to-noise ratio. By demodulating I / Q axis signals independently with real-valued single-input single-output (SISO) processing, it requires only about 15% DSP complexity and achieves intensity-modulation and direct-detection comparable. SISO processing also has the potential to transmit I and Q components from separate devices or satellites, enabling a flexible satellite communication network. The results demonstrate that the proposed architecture achieves detection sensitivities of -40.8 dBm for 80 Gbps quadrature phase-shift keying transmission and -33.0 dBm for 160 Gbps 16-QAM transmission with Nyquist sampling, whereas the ID architecture can hardly work. The proposed architecture effectively balances satellite power constraints with DSP computational demands for high-speed mega-constellation communications.The rapid development of low earth orbit (LEO) satellite communication networks imposes stringent bandwidth, cost, and power consumption requirements. Conventional intradyne detection (ID) architectures struggle with high Doppler frequency shifts (DFSs), necessitating excessive sampling rates and complex digital signal processing (DSP), resulting in elevated power consumption. This study proposes an inter-satellite polarization division multiplexing self-homodyne detection (PDM-SHD) architecture that compensates for DFSs in the optical domain by co-transmitting a polarization-orthogonal carrier light. The proposed architecture could achieve Nyquist sampling and half-quantization noise, leading to a 53.9% reduction in analog-to-digital converter power consumption under 40 Gbps 16-QAM transmission with a 16 dB signal-to-noise ratio. By demodulating I / Q axis signals independently with real-valued single-input single-output (SISO) processing, it requires only about 15% DSP complexity and achieves intensity-modulation and direct-detection comparable. SISO processing also has the potential to transmit I and Q components from separate devices or satellites, enabling a flexible satellite communication network. The results demonstrate that the proposed architecture achieves detection sensitivities of -40.8 dBm for 80 Gbps quadrature phase-shift keying transmission and -33.0 dBm for 160 Gbps 16-QAM transmission with Nyquist sampling, whereas the ID architecture can hardly work. The proposed architecture effectively balances satellite power constraints with DSP computational demands for high-speed mega-constellation communications.
Advanced Photonics Nexus
- Publication Date: Oct. 02, 2025
- Vol. 4, Issue 5, 056010 (2025)
Research Articles
Machine learning multitarget optimization for ultrashort pulse nonlinear dynamics in optical fibersLiang Zhao, Senyu Wang, Hao Lei, Hongyu Luo, Jianfeng Li, and Yong Liu
The design and optimization of nonlinear fiber laser sources, such as soliton self-frequency shift (SSFS) tunable sources and supercontinuum (SC) sources, have traditionally relied on manual tuning and simulations, posing challenges for real-time applications. Machine learning has shown promise in fiber nonlinear propagation characterization, but the optimization and design of nonlinear systems remain relatively unexplored, especially under multitarget optimization conditions. In this paper, we propose a method that combines deep reinforcement learning (DRL) and deep neural network (DNN) to achieve fast synchronization optimization of ultrafast pulse nonlinear propagation in optical fibers under multitarget optimization tasks, with applications demonstrated in complex SSFS and SC generation systems in the mid-infrared band. The results indicate that a set of optimization parameters can be obtained in a few seconds, enabling rapid, automated tuning of pulse parameters in pursuit of diverse optimization objectives. This integration of DRL and DNN models holds transformative potential for the real-time optimization of not only fiber lasers but also a wide variety of complex photonic systems, paving the way for intelligent, adaptive optical system design and operation.The design and optimization of nonlinear fiber laser sources, such as soliton self-frequency shift (SSFS) tunable sources and supercontinuum (SC) sources, have traditionally relied on manual tuning and simulations, posing challenges for real-time applications. Machine learning has shown promise in fiber nonlinear propagation characterization, but the optimization and design of nonlinear systems remain relatively unexplored, especially under multitarget optimization conditions. In this paper, we propose a method that combines deep reinforcement learning (DRL) and deep neural network (DNN) to achieve fast synchronization optimization of ultrafast pulse nonlinear propagation in optical fibers under multitarget optimization tasks, with applications demonstrated in complex SSFS and SC generation systems in the mid-infrared band. The results indicate that a set of optimization parameters can be obtained in a few seconds, enabling rapid, automated tuning of pulse parameters in pursuit of diverse optimization objectives. This integration of DRL and DNN models holds transformative potential for the real-time optimization of not only fiber lasers but also a wide variety of complex photonic systems, paving the way for intelligent, adaptive optical system design and operation.
Advanced Photonics Nexus
- Publication Date: Oct. 06, 2025
- Vol. 4, Issue 5, 056011 (2025)
Research Articles
High-speed and low-latency optical feature extraction engine based on diffraction operators | On the CoverRun Sun, Yuemin Li, Tingzhao Fu, Yuyao Huang, Wencan Liu, Zhenmin Du, Sigang Yang, and Hongwei Chen
Feature extraction in the optical domain offers a promising low-latency, high-throughput solution. Optical diffraction-based feature extraction operating under a coherent light source can further achieve parallel outputs with low energy consumption. However, it presents significant challenges for maintaining the coherent input, scaling the operation rates beyond 10 GHz, and ensuring the effective extraction of functional configuration simultaneously. We propose an optical feature extraction engine (OFE2), which is composed of a diffraction operator and a data preparation module, powering high-speed feature extraction for both image and temporal series tasks. This OFE2 can achieve a core latency of less than 250.5 ps; in addition, it can reach a throughput of 250 GOPS and an efficiency of 2.06 TOPS/W. Supported by the OFE2, a novel feature extraction paradigm is emerging, enabling high-speed, low-latency service access for applications in scene recognition, medical assistance, and digital finance.Feature extraction in the optical domain offers a promising low-latency, high-throughput solution. Optical diffraction-based feature extraction operating under a coherent light source can further achieve parallel outputs with low energy consumption. However, it presents significant challenges for maintaining the coherent input, scaling the operation rates beyond 10 GHz, and ensuring the effective extraction of functional configuration simultaneously. We propose an optical feature extraction engine (OFE2), which is composed of a diffraction operator and a data preparation module, powering high-speed feature extraction for both image and temporal series tasks. This OFE2 can achieve a core latency of less than 250.5 ps; in addition, it can reach a throughput of 250 GOPS and an efficiency of 2.06 TOPS/W. Supported by the OFE2, a novel feature extraction paradigm is emerging, enabling high-speed, low-latency service access for applications in scene recognition, medical assistance, and digital finance.
Advanced Photonics Nexus
- Publication Date: Oct. 08, 2025
- Vol. 4, Issue 5, 056012 (2025)
Research Articles
Direct generation of 1120-nm region mode-locked laser based on Yb-doped fiber with phase-biasing technologyRuichen Zhang, Mingyue He, Jing Su, Yongjia Yao, Xinyun Yang, Luming Song, Hang Wang, Kunchi Li, and Zhipeng Dong
Ytterbium (Yb)-based mode-locked fiber lasers have undergone significant development and found widespread applications owing to their high efficiency, compact size, and low cost. However, these lasers typically operate within the 1030 to 1080 nm range, and expanding their operational wavelength is crucial for applications across various fields. We present the direct generation of a mode-locked laser at 1120.06 nm using an all-polarization-maintaining structure, establishing the longest wavelength reported to date for Yb-doped fiber-based mode-locked lasers. A stable picosecond pulse laser at 1120 nm was realized by combining high-concentration Yb-doping and phase-biasing technology within a figure-9 cavity configuration. The laser delivers a pulse duration of 6.20 ps, a spectral width of 0.19 nm centered at 1120.06 nm, and a repetition rate of 21.52 MHz and reaches a maximum output power of 1.39 W via a double-cladding Yb fiber power amplifier in a master oscillator power amplifier configuration. Furthermore, we present a theoretical investigation of the laser performance, with simulation results aligning well with experimental findings. In addition, a 560.06-nm ultrafast yellow-green laser was generated through frequency doubling in a lithium triborate crystal. We present an approach for long-wavelength Yb-doped mode-locked lasers, with the potential to advance the development and application of Yb-based fiber lasers.Ytterbium (Yb)-based mode-locked fiber lasers have undergone significant development and found widespread applications owing to their high efficiency, compact size, and low cost. However, these lasers typically operate within the 1030 to 1080 nm range, and expanding their operational wavelength is crucial for applications across various fields. We present the direct generation of a mode-locked laser at 1120.06 nm using an all-polarization-maintaining structure, establishing the longest wavelength reported to date for Yb-doped fiber-based mode-locked lasers. A stable picosecond pulse laser at 1120 nm was realized by combining high-concentration Yb-doping and phase-biasing technology within a figure-9 cavity configuration. The laser delivers a pulse duration of 6.20 ps, a spectral width of 0.19 nm centered at 1120.06 nm, and a repetition rate of 21.52 MHz and reaches a maximum output power of 1.39 W via a double-cladding Yb fiber power amplifier in a master oscillator power amplifier configuration. Furthermore, we present a theoretical investigation of the laser performance, with simulation results aligning well with experimental findings. In addition, a 560.06-nm ultrafast yellow-green laser was generated through frequency doubling in a lithium triborate crystal. We present an approach for long-wavelength Yb-doped mode-locked lasers, with the potential to advance the development and application of Yb-based fiber lasers.
Advanced Photonics Nexus
- Publication Date: Oct. 10, 2025
- Vol. 4, Issue 5, 056013 (2025)
Research Articles
GLSaT: a spectral-aware transformer-based network enabling highly efficient and precise inverse design in metasurface optical filtersJiahui Liao, Xucong Bian, Xiang’ai Cheng, Quanjiang Li, Yuting Jiang, Shaozhen Lou, Haoqian Wang, Zixiao Hua, Teng Li, Jiangbin Zhang, Zhongjie Xu, Yueqiang Hu, and Zhongyang Xing
The traditional forward design process of metasurface optical filters is computationally costly and time-consuming; therefore, inverse design based on deep learning (DL) can help accelerate the process. We propose the global- and local-spectrum-aware transformer (GLSaT), a DL model that concerns the intrinsic correlations within the spectral sequences, compensating the drawbacks of current networks that only focus on structure-to-spectrum mappings. With both inter- and intra-fragment attention mechanisms implemented, the GLSaT achieves 32.9% higher accuracy than fully connected networks in our reflection tests. It also demonstrates an inherent balance between predictive precision and computational efficiency, outperforming alternative architectures. Furthermore, our extensive experimental validations demonstrate its generalization capability across diverse metasurface functionalities. The GLSaT architecture shows great potential for enhancing the efficiency of data-driven metasurface inverse design in the future.The traditional forward design process of metasurface optical filters is computationally costly and time-consuming; therefore, inverse design based on deep learning (DL) can help accelerate the process. We propose the global- and local-spectrum-aware transformer (GLSaT), a DL model that concerns the intrinsic correlations within the spectral sequences, compensating the drawbacks of current networks that only focus on structure-to-spectrum mappings. With both inter- and intra-fragment attention mechanisms implemented, the GLSaT achieves 32.9% higher accuracy than fully connected networks in our reflection tests. It also demonstrates an inherent balance between predictive precision and computational efficiency, outperforming alternative architectures. Furthermore, our extensive experimental validations demonstrate its generalization capability across diverse metasurface functionalities. The GLSaT architecture shows great potential for enhancing the efficiency of data-driven metasurface inverse design in the future.
Advanced Photonics Nexus
- Publication Date: Oct. 11, 2025
- Vol. 4, Issue 5, 056014 (2025)
Research Articles
High-sensitivity Er3+/Yb3+:La2O3-TiO2-Ga2O3-ZrO2 optical temperature sensors under high magnetic fieldYanzhuo Wang, Jun Wu, Jiqi Lu, Enze Kang, Xichen Xu, Qiuming Fu, Shenggao Wang, Zhibin Ma, Wubin Dai, Yibo Han, and Hongyang Zhao
Optical temperature sensor materials face great challenges in terms of temperature measurement sensitivity and applicability in extreme environments. To overcome these problems, Er3 + / Yb3 + co-doped La2O3-TiO2-Ga2O3-ZrO2 (LTGZ) glasses were designed and synthesized using the aerodynamic levitation method. In the glass system, the strongest intensity of upconversion luminescence was measured on 3.0Yb3 + / 0.5Er3 + (mole fraction) co-doped LTGZ glasses. In the temperature range of 300 to 700 K, the maximum relative and absolute sensitivities were 2.71 % and 0.56 % K - 1, respectively. The temperature reliability was proved through variable temperature cycling tests. More importantly, to our knowledge, it is the first time to investigate the optical temperature measurement capability under a high magnetic field in this as-designed sensor. By applying the magnetic field up to 42 T, the relative sensitivity changes from 1.79 % to 1.58 % K - 1, revealing that the temperature sensitivity of the sensor remains stable even in high magnetic fields. The results of the study provide a reference for the selection of temperature measurement materials in the field of optical temperature sensing, and the designed temperature sensor can be used for temperature measurement in extreme environments, especially in strong magnetic field conditions, which provides an important value for the development of special optical temperature sensors.Optical temperature sensor materials face great challenges in terms of temperature measurement sensitivity and applicability in extreme environments. To overcome these problems, Er3 + / Yb3 + co-doped La2O3-TiO2-Ga2O3-ZrO2 (LTGZ) glasses were designed and synthesized using the aerodynamic levitation method. In the glass system, the strongest intensity of upconversion luminescence was measured on 3.0Yb3 + / 0.5Er3 + (mole fraction) co-doped LTGZ glasses. In the temperature range of 300 to 700 K, the maximum relative and absolute sensitivities were 2.71 % and 0.56 % K - 1, respectively. The temperature reliability was proved through variable temperature cycling tests. More importantly, to our knowledge, it is the first time to investigate the optical temperature measurement capability under a high magnetic field in this as-designed sensor. By applying the magnetic field up to 42 T, the relative sensitivity changes from 1.79 % to 1.58 % K - 1, revealing that the temperature sensitivity of the sensor remains stable even in high magnetic fields. The results of the study provide a reference for the selection of temperature measurement materials in the field of optical temperature sensing, and the designed temperature sensor can be used for temperature measurement in extreme environments, especially in strong magnetic field conditions, which provides an important value for the development of special optical temperature sensors.
Advanced Photonics Nexus
- Publication Date: Oct. 22, 2025
- Vol. 4, Issue 5, 056015 (2025)
Research Articles
Speckle Transformer: direct classification through scattering media with limited informationQi Zhao, Zhiyuan Wang, Zhipeng Yu, Tianting Zhong, Haoran Li, Shengfu Cheng, Haofan Huang, Chi Man Woo, Huanhao Li, and Puxiang Lai
Retrieving high-fidelity images from optical speckles remains challenging, especially when the information in speckles is severely insufficient. To address classification through scattering media under such constraints, we propose Speckle Transformer, a vision-transformer-based model that directly classifies objects using raw speckle patterns without intermediate image retrieval. By leveraging inherent features within speckles to extract discriminative features, our approach achieves nearly 90% accuracy for classifying speckles encoded with different images, outperforming traditional retrieval-classification pipelines by up to five times, even with extreme information sparsity (i.e., 1/1024 speckle regions of interest). In addition, we quantify speckle information capacity via information entropy analysis, demonstrating that classification accuracy correlates strongly with the information entropy of speckle autocorrelation. We not only overcome limitations of conventional methods but also establish a paradigm for real-time classification in scattering environments with constrained data.Retrieving high-fidelity images from optical speckles remains challenging, especially when the information in speckles is severely insufficient. To address classification through scattering media under such constraints, we propose Speckle Transformer, a vision-transformer-based model that directly classifies objects using raw speckle patterns without intermediate image retrieval. By leveraging inherent features within speckles to extract discriminative features, our approach achieves nearly 90% accuracy for classifying speckles encoded with different images, outperforming traditional retrieval-classification pipelines by up to five times, even with extreme information sparsity (i.e., 1/1024 speckle regions of interest). In addition, we quantify speckle information capacity via information entropy analysis, demonstrating that classification accuracy correlates strongly with the information entropy of speckle autocorrelation. We not only overcome limitations of conventional methods but also establish a paradigm for real-time classification in scattering environments with constrained data.
Advanced Photonics Nexus
- Publication Date: Oct. 27, 2025
- Vol. 4, Issue 5, 056016 (2025)
Vol. 5, Iss.1-Jan..1,2026
Research Articles
4D event imaging with a single neuromorphic cameraRaviv Ilani and Adrian Stern
Neuromorphic cameras, or dynamic vision sensors, are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level. Recently, they garnered increasing interest due to their extremely high temporal resolution, wide dynamic range, low power consumption, and high pixel bandwidth. Despite their advantages, most existing three-dimensional (3D) event imaging solutions rely on multicamera configurations, which are costly, complex, and challenging to synchronize. In this study, we introduce a new framework for four-dimensional (4D) event imaging using a single static neuromorphic camera. We take advantage of the inherent sparsity of event data to combine optically encoded stereo channels into a single event camera. By utilizing optical channel multiplexing, we maintain sensor resolution while retaining the key advantages of event cameras.Neuromorphic cameras, or dynamic vision sensors, are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level. Recently, they garnered increasing interest due to their extremely high temporal resolution, wide dynamic range, low power consumption, and high pixel bandwidth. Despite their advantages, most existing three-dimensional (3D) event imaging solutions rely on multicamera configurations, which are costly, complex, and challenging to synchronize. In this study, we introduce a new framework for four-dimensional (4D) event imaging using a single static neuromorphic camera. We take advantage of the inherent sparsity of event data to combine optically encoded stereo channels into a single event camera. By utilizing optical channel multiplexing, we maintain sensor resolution while retaining the key advantages of event cameras.
Advanced Photonics Nexus
- Publication Date: Nov. 26, 2025
- Vol. 5, Issue 1, 016001 (2026)
Research Articles
High-contrast optical coherence tomography angiography via log-scale inverse static-to-dynamic ratio analysis for weak flow signalJinyu Fan, Jiangjie Huang, Ning Tang, Jingye Gu, Lina Xing, Yi He, and Guohua Shi
The conventional optical coherence tomography angiography (OCTA) algorithm is implemented in the linear domain, which may lead to the neglect of weak blood flow information. Logarithmic transformation is widely used in signal analysis to improve the contrast of weak signals. However, decorrelation-based OCTA in the logarithmic domain is also sensitive to the signal-to-noise ratio (SNR) even in high SNR regions, introducing strong flow artifacts that severely reduce the blood vessel contrast. A metric—static-to-dynamic ratio (SDR)—was used to quantify weak flow signals, and a weak flow model among decorrelation, SDR, and SNR was established. Based on this model, we proposed a log-scale inverse SDR-based OCTA method (logiSDR-OCTA), which simultaneously and effectively reduces SNR-induced flow artifacts in static regions and prevents the attenuation of the flow signal in dynamic regions. The in vivo imaging experiments demonstrated that the contrast of the mouse brain logiSDR images was 2.43 times that of linear-scale decorrelation images and 2.71 times that of log-scale subtraction images; the contrast of the human retina logiSDR images was 4.91 times that of linear-scale decorrelation images and 3.56 times that of log-scale subtraction images.The conventional optical coherence tomography angiography (OCTA) algorithm is implemented in the linear domain, which may lead to the neglect of weak blood flow information. Logarithmic transformation is widely used in signal analysis to improve the contrast of weak signals. However, decorrelation-based OCTA in the logarithmic domain is also sensitive to the signal-to-noise ratio (SNR) even in high SNR regions, introducing strong flow artifacts that severely reduce the blood vessel contrast. A metric—static-to-dynamic ratio (SDR)—was used to quantify weak flow signals, and a weak flow model among decorrelation, SDR, and SNR was established. Based on this model, we proposed a log-scale inverse SDR-based OCTA method (logiSDR-OCTA), which simultaneously and effectively reduces SNR-induced flow artifacts in static regions and prevents the attenuation of the flow signal in dynamic regions. The in vivo imaging experiments demonstrated that the contrast of the mouse brain logiSDR images was 2.43 times that of linear-scale decorrelation images and 2.71 times that of log-scale subtraction images; the contrast of the human retina logiSDR images was 4.91 times that of linear-scale decorrelation images and 3.56 times that of log-scale subtraction images.
Advanced Photonics Nexus
- Publication Date: Dec. 04, 2025
- Vol. 5, Issue 1, 016002 (2026)
Research Articles
Solid 226 nm laser pumped by a Nd:YAG laser for two-photon absorption detection of oxygenYi Zhang, Shaoyi Wang, Minghao Yu, Zhongqi Feng, and Dacheng Zhang
We present a solid 226 nm deep ultraviolet laser system pumped by a Nd:YAG laser. A diamond Raman laser with a 1485 nm wavelength was generated up to 2.53 mJ pumped by a 9.7 mJ 1064 nm laser, which is the highest pulse energy of a second Stokes diamond Raman laser pumped by a 1064 nm laser as we know. Then, the Raman laser is mixed with the frequency-quadrupled 1064 nm laser to produce the 226 nm laser. The maximum output pulse energy at 226 nm reaches 0.49 mJ. The overall conversion efficiency from 1064 to 226 nm is up to 1.14%, which is significantly higher than conventional optical parametric oscillator technology for the generation of 226 nm laser. The 226 nm laser system has been used in a laser-induced fluorescence (LIF) experiment of oxygen two-photon to demonstrate its potential for LIF measurements.We present a solid 226 nm deep ultraviolet laser system pumped by a Nd:YAG laser. A diamond Raman laser with a 1485 nm wavelength was generated up to 2.53 mJ pumped by a 9.7 mJ 1064 nm laser, which is the highest pulse energy of a second Stokes diamond Raman laser pumped by a 1064 nm laser as we know. Then, the Raman laser is mixed with the frequency-quadrupled 1064 nm laser to produce the 226 nm laser. The maximum output pulse energy at 226 nm reaches 0.49 mJ. The overall conversion efficiency from 1064 to 226 nm is up to 1.14%, which is significantly higher than conventional optical parametric oscillator technology for the generation of 226 nm laser. The 226 nm laser system has been used in a laser-induced fluorescence (LIF) experiment of oxygen two-photon to demonstrate its potential for LIF measurements.
Advanced Photonics Nexus
- Publication Date: Dec. 08, 2025
- Vol. 5, Issue 1, 016003 (2026)
Theme Issue on Time-Varying Photonics (2025)
Submission Open:10 December 2025; Submission Deadline: 30 April 2026
Submission Open:1 July 2025; Submission Deadline: 31 October 2025
Editor (s): Linjie Zhou, Xianshu Luo
Vol. 4, Issue 5, 056007 (2025)
Vol. 4, Issue 4, 046010 (2025)
Vol. 4, Issue 4, 046004 (2025)
Intelligent soliton molecules control in an ultrafast thulium fiber laserOn the Cover
Vol. 4, Issue 1, 016012 (2025)
Vol. 4, Issue 3, 036009 (2025)
Vol. 4, Issue 3, 036012 (2025)
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