Real-time characterization of temporal behaviors of ultrafast lasers is an important and challenging task. Existing methods are typically limited to a few spatial modes or a two-dimensional (2D) space, which is insufficient for adequately portraying the propagation of ultrafast lasers in a medium when superluminal motion is involved. To accurately characterize the propagation of ultrafast lasers in the presence of superluminal motion, it is necessary to record the complete four-dimensional (4D) space-time (x, y, z, t) in which the laser pulse exists. However, most ultrafast cameras are incapable of three-dimensional (3D) imaging. Additionally, when 2D imaging is performed, the tradeoff between the light throughput and imaging depth of field can be a hindrance for capturing superluminal motions, which typically occurs when the light is propagating at a large angle with respect to the camera plane. The objectives of this study were to develop efficient 3D ultrafast imaging methods that can capture the complete 4D space-time with an extended depth of field and to record and analyze superluminal motions with a high spatiotemporal resolution.

Light field tomography (LIFT), which leverages intelligent algorithms and novel optics, is a new method capable of 3D imaging of ultrafast phenomena with a picosecond-scale temporal resolution. The core idea of LIFT is to reformulate image formation as a computed-tomography problem. In LIFT, the spherical lens is replaced with a cylindrical lens, allowing each pixel to record a line integral of the scene along the direction of the invariant axis of the lens (one without optical power). With a one-dimensional (1D) sensor placed at the focal plane of the cylindrical lens, a parallel beam projection of the scene can be obtained, similar to that in X-ray computed tomography. By using a linear array of such cylindrical lenses, each oriented at a distinct angle with respect to the 1D sensor, projection data along different angles can be simultaneously recorded for computationally reconstructing the scene. Meanwhile, the light field information of the scene is obtained by the cylindrical lens array, which allows 3D depth extraction at each time instant; thus, complete 4D imaging is achieved. With an intelligent optimization algorithm, we improved the 3D imaging quality and achieved a light field image resolution of 128×128×7 with a time sequence of 1016 points, allowing the imaging depth of field to be increased approximately sevenfold.

Using LIFT, we captured the reflection of a picosecond laser pulse upon incidence on a mirror in 3D space. As the laser pulse propagates away from the camera, the measured propagation speed of the laser pulse is lower than the actual speed of light, and the speed is further reduced after the laser is reflected by the mirror, which modifies the propagation angle of the laser pulse. Such superluminal motion is made even more evident by coupling the laser into a multimode light-diffusing-fiber and then winding the fiber in a round-trip fashion. In this case, the forward propagation of the laser has a speed of only approximately 36% of the actual speed of light in the fiber, whereas the reverse propagation appears to be significantly faster: the apparent velocity is 135% of the actual speed, which is far faster than the forward propagation. Interestingly, the light field capability of LIFT is found to be critical for clearly resolving the fiber structure, as conventional imaging of the entire fiber structure with a single focal plane induces significant defocus blur. Moreover, we examined the laser pulse broadening inside the multimode fiber. While pulse broadening is common in multimode fibers, doping the light-diffusing fiber core with nanostructures for scattering light accelerates the broadening: within a propagation time of 720 ps, the picosecond laser pulse width increases from 10 ps to 50, 72, and 88 ps at three different points on the fiber. This is similar to laser pulse broadening after propagation through a thin scattering medium.

Using the novel LIFT method, we demonstrated in this study that it is important to consider the 3D nature of light propagation when characterizing the spatiotemporal behaviors of ultrafast lasers. Without accurate 3D information of the laser pulses, the observed speed of light propagation depends heavily on the imaging geometry and the propagation angle of the light. The resultant superluminal motion will distort the spatiotemporal profile of the ultrafast laser pulse, leading to inaccurate interpretations of the dynamics of the laser inside the medium. With accurate 3D information, LIFT can correctly identify the superluminal motions of the ultrafast laser and clarify the temporal variations of the laser pulse at each spatial position, owing to its high spatial resolution (128×128), large depth of field, and deep time sequence (up to 1016 points) acquired with a single snapshot. This paves the way for fully correcting the spatiotemporal distortion of ultrafast lasers caused by superluminal motions, which we aim to achieve in future research.

The invention of laser technology has had a transformative impact on society. Mode-locked fiber lasers have been widely used in research and industry, and they play an important role in basic science as a convenient nonlinear system. A mode-locked fiber laser is a complex nonlinear dissipative system with a large number of internal nonlinear dynamical phenomena that, in addition to outputting stable femtosecond pulses, exhibits a series of complex mode-locked states, including the breather locked mode, strange waves, noise-like locked modes, soliton explosions, and self-organized modes arising from soliton interactions, such as soliton molecules, soliton crystals, soliton complexes, and supramolecular structures. Even chaotic states have recently been discovered in mode-locked lasers. The study of these mode-locked states helps to understand the nonlinear dynamical properties of femtosecond fiber lasers. Additionally, because the femtosecond fiber laser is a universal nonlinear dissipative system, studying its dynamics can clarify the complex dynamics in related fields, such as Bose-Einstein condensation, microcavities, and oceanography. The intrinsic dynamics of these systems and the mode-locked laser are described uniformly by the nonlinear Schr?dinger equation and thus have similarities.

Owing to the presence of numerous mode-locked regions in mode-locked lasers, it has long been a challenging problem to control the parameters of the laser and thus access specific mode-locked states. For example, the most commonly used femtosecond fiber laser based on the nonlinear polarization rotational mode-locking technique is mathematically a multidimensional parametric space and experimentally requires tuning of at least seven parameters (pump, loss, dispersion, nonlinearity, and angles of the three waveplates) to traverse the entire parametric space. Because of the lack of a definite functional relationship between the mode-locking state and these parameters, a long trial-and-error process is needed to obtain the desired mode-locking state. In addition, even if the target locked mode is obtained, its repeatability is a problem.

Recently, a major breakthrough was made in intelligent mode-locked lasers, which can resolve the difficulty of precise control of mode-locked states. In 2015, Prof. Grelu’s group in France applied a genetic algorithm to the intelligent control of mode-locked lasers for the first time and realized the intelligent control of soliton pulses and noise-like pulses. Subsequently, the development of intelligent mode-locked lasers has accelerated. Hence, it is necessary to summarize the existing studies to rationally guide subsequent research in this area.

The principle of the commonly used smart locking algorithms and recent scientific research results are summarized. First, the principles of the genetic algorithm, human-like algorithm, and artificial neural network are explained, and a schematic (Fig.1) and architecture diagram (Fig.2) are presented. Then, recent scientific achievements in smart mode-locked lasers are described, including the first implementation of a soliton-locked mode in smart lasers by Andral et al. at the Université de Bourgogne, France (Fig.3); the development of genetic algorithms for soliton-locked mode recovery by Winters et al. at Kapteyn-Murnane Laboratories, USA (Fig.4); the development of the first smart programmable mode-locked laser by Pu et al. at Shanghai Jiao Tong University (Fig.5); and the development of the first smart programmable mode-locked laser using deep learning for intelligent mode-locking recovery by Yan et al. at the National University of Defense Technology (Fig.6). Subsequently, the realization of programmable control of the spectral width and spectral shape by Pu et al. of Shanghai Jiao Tong University (Fig.7) and the intelligent control of spatiotemporal mode-locking by Wei et al. of South China University of Technology (Fig.8) are elaborated. The intelligent regulation of the breather ultrafast laser is summarized, starting with the design of an adaptation function based on the radiofrequency signal of the breather locked mode (Fig.9), in which the relaxation oscillation dynamics and noise-like pulse dynamics in the laser are excluded (Fig.10). Then, experimental results of the genetic algorithm (Fig.11) are discussed, along with the control of the breather breathing ratio, the breathing period, and the number of pulses (Figs.12-14). Finally, the work related to the intelligent control of fractal respiratory subsets is briefly described. The differences in the spectra and stability of frequency-locked and non-frequency-locked breathers are examined (Figs.15 and 16), the evolutionary dynamics of fractal breathers are specified (Fig.17), and the intelligent search for fractal breathers is implemented using a smart laser based on a liquid-crystal phase delay (Figs.18 and 19).

This paper reviews the application of intelligent-control technology in passively mode-locked fiber lasers. Using intelligent-control technology, the automatic generation and control of the mode-locked state can be realized without manual tuning, which reduces the tuning time of the laser and improves the tuning accuracy as well as the repeatability of the mode-locked state. This self-optimizing ultrashort pulse laser has promising applications in certain environments. Although the passive mode-locked fiber laser is a complex dynamical system, the successful achievement of accurate tuning of multiple mode-locked states by genetic algorithms indicates the universality of these algorithms. A series of intelligent algorithms, including genetic algorithms, are expected to be applied to the intelligent control of more complex mode-locked states. The current intelligent-control technology focuses on controlling lasers and achieving automatic laser tuning. Whether intelligent-control techniques can have an impact on laser physics remains an open question.

Passively mode-locked fiber lasers are typical nonlinear systems with abundant physical phenomena such as soliton collisions, soliton molecules, and soliton explosions. With the rise of ultrafast detection technologies such as the time-stretching dispersion Fourier transform (TS-DFT), the number of soliton dynamics phenomena has increased, generating large amounts of analyzable data. Laser self-tuning is an important method for optimizing laser mode-locking; however, traditional algorithms limit the efficiency of laser self-tuning. Thus, it is necessary to reduce the dimensions of high-dimensional data and extract features to reduce irrelevant and redundant parameters in complex nonlinear systems. Furthermore, using an autoencoder to study the interaction processes of dissipative solitons in a passively mode-locked fiber laser can not only extract the main characteristic parameters of the soliton structure but also enhance the physical analysis ability of the network by mining the relationship between the full connection layer parameters and the soliton characteristic parameters.

This study proposes a passively mode-locked fiber laser that operates in the 1550 nm wavelength band based on nonlinear polarization rotation technology. The total cavity length, dispersion, and repetition frequency of the laser were 7.9 m, -0.133 ps², and 26.8 MHz, respectively. When the output power of the fixed pump source was 127 mW, three soliton bound states were obtained by adjusting the polarization controller. Additionally, multiple sets of real-time spectral information was obtained using TS-DFT technology. The solitons exhibited obvious interference fringes owing to spectral coherence superposition. We observed and collected data on the dynamics of different soliton bound states, thereby introducing a large amount of analyzable data into the network model. Furthermore, we designed an evolutionary convolutional autoencoder model based on the operational methods of convolution and pooling in neural networks. The model was comprised of two parts: a dynamic encoder, which compresses the input multidimensional data through a convolutional transformation for feature compression, and a propagation decoder, which generates convolutional kernels and bias matrices using the feature parameters. The initial spectrum was then convolved layer-by-layer and finally reconstructed into multidimensional data. By minimizing the deviation between the input and output spectral matrices for network learning, data dimensionality reduction and system evolution feature extraction can be achieved.

An evolutionary convolutional autoencoder model was used to extract characteristic parameters from the dynamics of different soliton bound states, and they were predicted and reconstructed them. After 200 iterations, the training and testing losses were approximately 0.0952 and 0.1017, respectively. Through continuous parameter debugging, we found that the network was most effective when the number of latent parameters was 35. We believe that there is a correspondence between this and the dimensions of the parameter space in dissipative systems. The reconstructed spectrum showed an interference stripe distribution and changes similar to the actual spectrum, with an average Pearson correlation coefficient of 98.52%. To further characterize the effectiveness of the network structure reconstruction, Fourier transforms were applied to the original and reconstructed spectra to obtain their autocorrelation traces and phase difference evolution curves. The phase evolution information of the original and reconstructed spectra was consistent, and the network model reproduced the high-frequency oscillation dynamics of the soliton pairs.

In this study, a 1550 nm band passively mode-locked fiber laser was developed based on nonlinear polarization rotation technology. The dynamics of the soliton bound states in the laser were measured using TS-DFT real-time detection technology, and the evolution of the soliton spacing and phase difference was analyzed based on the autocorrelation algorithm. Simultaneously, a design for an evolutionary convolution self-coding model was presented for feature extraction and the prediction of soliton bound state dynamics. This study provides new insights into soliton dynamics and helps to explore the physical mechanisms of soliton interactions.

Phase control is a key factor in achieving coherent beam combining. Recently, the number of coherent combining paths has been continuously expanding, and the achieved combining power has been continuously increasing. However, when the power of a single combining light source exceeds kilowatts or even several kilowatts, the residual of the phase-locked control system significantly increases with the complexity of the application environment. With the rapid development of artificial intelligence technology, exploring new phase control methods based on machine learning has become a new development trend.

In 2019, Tünnermann et al. introduced reinforcement learning into coherent combining systems, achieving the prediction and compensation of phase noise below kHz (Fig. 1). In 2021, the team validated the feasibility of applying the reinforcement learning phase-locked control method to tiled-aperture coherent combining systems in a simulation environment and explored the ability of the control method to achieve combining light field shaping (Fig. 2). To overcome the limitations of reinforcement learning in expanding the number of coherent combining units, in 2021, Shpakovych et al. proposed a two-dimensional phase dynamic control scheme based on neural networks. This scheme uses a quasi-reinforcement learning method based on neural networks, and the phase-locked residual can reach up to λ/30 (Fig. 3). In 2022, Shpakovych et al. implemented the phase control of a seven-channel fiber amplifier array using a quasi-reinforcement learning algorithm (Fig. 4).

To test the feasibility of phase locking using deep learning in energy-type fiber laser coherent combining systems, in 2019, Hou et al. introduced deep learning into coherent combining systems for the first time and achieved phase locking (Fig. 6). Subsequently, the Chinese Academy of Sciences in China, the Berkeley National Laboratory in the United States, and the University of Southampton in the United Kingdom conducted the concept or experimental verification of phase-locking based on deep learning.

In addition to energy-based applications, the large array element characteristics and ability to quickly adjust the sub-beam phase of coherent combining systems provide a novel technical approach for the generation and customization of special light fields with high power and high mode switching speed. To solve the failure of light field control caused by phase conjugation, Hou et al. proposed the concept of phase-locked control evaluation function based on non-focal plane extraction. Further, they extended the evaluation function of power in the bucket widely used in the study of energy concentrated spot generated by conventional coherent combining to a generalized evaluation function suitable for complex light field customization, achieving decoupling control of the laser array conjugation phase. The feasibility of generating complex light fields such as orbital angular momentum beams was demonstrated. In 2020, Chang et al. proposed the problem of phase conjugation decoupling in the generation of coherent array special light fields using scatterers. With the application of artificial intelligence algorithms in energy-based coherent combining systems, introducing them into the array light field control of special beams to address complex phase control problems has become a new research approach.

In 2020, Hou et al. introduced deep learning algorithms into fiber laser arrays to achieve optical field regulation through a two-step phase control (Fig. 11). To further investigate the optical field information, in 2022, Hou et al. customized orbital angular momentum (OAM) beams from an angle domain perspective and introduced deep learning algorithms to learn the mapping of the relative phase from intensity information to array unit beams (Fig. 12). The purity of the OAM mode in the later stages of phase control using angular domain information has been improved, verifying that angular domain light field information is helpful to control the phase accurately.

Currently, significant results have been achieved in the design and preliminary verification of improving the phase control capability of fiber laser arrays based on machine learning. The number of control paths has exceeded 100, and it demonstrates better performance than traditional optimization algorithms in terms of control speed and control accuracy. However, issues still exist to be addressed in performance verification under high-power medium/strong noise conditions, and the system verification of larger array elements is urgently required. With the development of artificial intelligence technology, the comprehensive improvement in the sample training speed, capacity, accuracy, and mining accuracy is expected to promote machine learning to play a greater potential role in array laser phase regulation.

Adaptive optics (AO) technology enhances imaging quality by measuring and compensating for wavefront errors. It has been widely used in ground-based telescopes, biological imaging, ocular aberration correction, and laser communication,and so on.

Current AO systems can be categorized into two groups depending on the presence or absence of a wavefront sensor (WFS). Wavefront sensorless (WFSless) AO technology acquires the pupil phase via a retrieval algorithm based on the light intensity distribution. This type of technology can be divided into two kinds: single-image-based and phase-diversity-based technology. Single image-based technology measures the wavefront errors through a single intensity image. However, the phase distribution obtained from a solitary intensity image follows a one-to-many mapping relationship, resulting in limited accuracy. On the other hand, the phase-diversity-based AO technique can determine the phase distribution of the optical field on the input plane by collecting image information of the focal plane and the defocusing plane, resulting in a higher detection accuracy. However, a large number of iterations and measurements are required to obtain optimal results using traditional WFSless AO technology, making it unsuitable for high-speed and real-time scenarios. WFS AO technology employs a WFS based on the interference principle or a traditional geometric optics principle to measure the wavefront. Examples of WFSs used include phase-shifting interference WFSs, Shack-Hartmann WFSs (SHWFSs), and pyramid WFSs (PyWFSs). A high measurement accuracy is achieved using the traditional phase-shifting interference WFS method, but its real-time performance is suboptimal and is susceptible to environmental disturbances. The SHWFS is widely used in AO systems due to its simple structure and ease of operation. However, as a result of its pupil segmentation mechanism, the spatial resolution of the image is low and the dynamic range is small. While the PyWFS can detect weaker light than SHWFS AO technology, it is expensive and has a small linear range in the unmodulated mode.

Recently, the rapid development of artificial intelligence has accelerated development in various fields. Deep learning technology, a significant branch of artificial intelligence, has exhibited remarkable capabilities in search, data mining, machine translation, and speech recognition. Deep learning algorithms are founded on artificial neural networks, which optimize weights and biases based on the given sets of samples. The neural network, after being trained with vast amounts of data, can accurately establish the input-output relationship. Despite the prolonged training time, results can be inferred quickly, making it useful in a multitude of technical domains. The combination of AO and deep learning technology is expected to overcome the issues encountered in conventional AO techniques. It is hypothesized that deep learning can lead to faster and more precise wavefront correction, thereby enhancing the performance of AO technology.

This review introduces several popular artificial neural networks (Fig. 1) used in deep learning. The ways in which deep learning has been combined with AO technology are classified into two categories: techniques with and techniques without WFS. The WFSless category is subdivided into single-image-based (Figs. 3-4) and phase-diversity-based (Figs. 5-6) technologies, while the WFS category includes examples of SHWFSs (Figs. 7-9) and other WFS technologies combined with deep learning. Moreover, the review introduces a new diffraction neural network (Fig. 10), building on the traditional neural network, and provides examples of how this diffraction neural network has been combined with AO technology. The review notes that, over the past five years, examples of deep learning combined with AO technology have focused on improving the real-time performance and accuracy of traditional AO techniques. Finally, the review discusses the future development directions for deep learning-based AO technology.

Utilizing deep learning with WFSless AO technology provides several favorable advantages, such as its simple structure and low cost. While the single-image-based method only uses one image to correct the aberration, the corresponding phase of the intensity image reveals a one-to-many mapping, ultimately resulting in inaccurate calculations. On the other hand, the phase-diversity-based method uses two images with known phase differences to determine the size of the aberrations, yielding more accurate results than via the single-image-based method. Within the WFS AO technology field, numerous SHWFS-based methods exist. With a focus on improving the accuracies of centroid position and wavefront reconstruction, the application of deep learning networks has accelerated and further improved accuracy. Wavefront measurement methods based on sensors other than the SHWFS have gradually been integrated with deep-learning technology.

In the future, deep learning algorithms will be combined with other technologies, including reinforcement learning, and applied to other types of sensors such as the PyWFS to further enhance AO performance. Furthermore, AO will likely be integrated with a novel optical neural network to optimize its performance. Despite the growing body of literature on deep-learning based AO, most studies have been limited to simulation data; thus, it is imperative to evaluate deep-learning based AO using real-world scenarios. Moreover, while current AO technology focuses on the correction of point-source wavefront errors, the detection of extended-source wavefront errors should also be explored in future developments.

Metamaterial design and fiber beam control are two important topics in the study of optical field manipulation. Metamaterials are artificial materials with periodic structures and physical properties that do not exist naturally in the world. Suitable structural designs are crucial for achieving the potential of metamaterials. Numerical calculations and parameter optimization methods such as finite difference time domain (FDTD), finite element method (FEM), rigorous coupled wave analysis (RCWA), and genetic algorithms are commonly used in metamaterials design. However, these methods suffer from high computational costs and strong dependence on expert experience. Specifically, the high computational cost is due to the complexity of solving partial differential equations, while the reliance on expert experience stems from the fact that these numerical calculation methods depend on physical modeling. Additionally, parameter optimization algorithms also suffer from high computational costs due to the explosion of parameter combinations and repeated calls to numerical computation methods. Therefore, many researchers have turned to deep learning methods, attempting to use a data-driven approach to allow neural network models to learn the mapping relationship between metamaterial structure and optical response during the feature learning process, thus achieving accurate and efficient metamaterial design while shielding underlying physical details.

Fiber beam control refers to adjusting parameters such as amplitude, phase, and polarization of a fiber optic beam to obtain novel features or stable states. Traditional methods mainly include genetic algorithms, stochastic parallel gradient descent (SPGD) algorithm, PID, and other search methods, which are limited by their inability to effectively solve system control problems in complex environments, i.e., speed and accuracy issues. These optimization methods have simple strategies that are unable to generate good behavioral paths, resulting in too many steps to reach the target state. Moreover, they mechanically respond to environmental states, making them vulnerable to system noise interference and limiting the accuracy of system output. Deep reinforcement learning overcomes these limitations by introducing a learning mechanism that can actively respond to environmental stimuli, making up for the shortcomings of traditional methods. Fiber beam control is a dynamic process that can be abstracted into a state machine, which is naturally suitable for control methods based on deep reinforcement learning. Therefore, for such or even more complex systems, deep reinforcement learning-based methods have considerable application prospects.

Multi-layer perceptron (MLP) is a simple and basic neural network model widely used in various metamaterial design works. Peurifoy et al. used MLP to complete the inverse design of an 8-layer spherical shell nanostructure [Fig. 7(a)]. Liu et al. proposed a method that combines forward prediction networks for spectra and inverse design networks for devices [Fig. 7(b)]. Du et al. developed a scalable multi-task learning (SMTL) model for designing low-dimensional nanostructures [Fig. 7(c)]. Zhao et al. released a data-enhanced iterative few-sample (DEIFS) algorithm based on data augmentation [Fig. 7(d)]. In addition to MLP, convolutional neural network (CNN) and generative adversarial network (GAN) are also commonly used network models. Zhu et al. proposed a transfer learning-based method for predicting metamaterials accurately and quickly using the pre-trained Inception V3 model on image data, achieving good results for binary metamaterial prediction [Fig. 8(a)]. Jiang et al. used GAN for the topology design of complex nano-devices, effectively solving the problem of time-consuming iterative optimization methods for designing complex devices, reducing design time by about 80% [Fig. 8(b)]. Sajedian et al. efficiently determined the optimal parameters for three-layer metamaterial devices among 23 different material types and geometry parameters using the double deep Q network (DDQN), greatly improving the computational transmittance efficiency of metamaterials [Fig. 8(c)]. Zhao et al. integrated the idea of reinforcement learning into the model and designed a data-enhanced deep greedy optimization (DEDGO) algorithm [Fig. 8(d)]. Sajedian et al. combined CNN with recurrent neural network (RNN) to predict the absorption spectra of nano-devices, which played an auxiliary role in device design [Fig. 8(e)].

In fiber beam control, the J. N. Kutz team at the University of Washington proposed using deep reinforcement learning algorithms to achieve automatic mode locking control of lasers from a simulation perspective in 2020 [Fig. 11(a)]. In 2021, the team led by researcher Jiang Tian at the National University of Defense Technology designed an automatic mode locking control laser system based on the DDPG strategy and the DELAY reinforcement learning algorithm [Fig. 11(b)]. In mid-2022, Li Zhan et al. from the Chinese Academy of Sciences designed a feedback control algorithm based on deep reinforcement learning and long short-term memory (LSTM) network models to stabilize the state of mode-locked lasers [Fig. 11(c)]. In the latter half of 2022, Luo Saiyu et al. from Nanjing University of Science and Technology applied the TD3 algorithm from deep reinforcement learning to an ultrafast green Ho:ZBLAN laser [Fig. 11(d)]. In 2023, the research team led by Jiang Tian at the National University of Defense Technology once again designed DRCON using reinforcement learning to control the stability of coherent optical neuron systems (Fig. 13).

This article focuses on recent research on deep learning in metamaterial design and fiber beam control. The introduction of deep learning has greatly promoted the development of both fields. Traditional methods face the following problems when dealing with increasingly complex optical systems: (1) inability to effectively transfer expert experience; (2) inability to avoid numerical calculations; and (3) a limited solvable problem space. Compared with traditional methods, deep learning methods can help isolate the underlying physical details to some extent, reducing the difficulty of design and control.

In recent years, the popularity of artificial intelligence (AI) has provided a new incentive for advances in science and technology in the laser industry, further promoting its rapid development and wide application. To present a clear view of the empowering effect of AI on lasers and facilitate further development of this emerging field, it is important to identify the advancements, opportunities, and challenges of AI-enabled lasers. Therefore, this work begins with a review of the progress in this field, from component design to system optimization and from laser property characterization to laser application. Then, we provide a preliminary analysis and outlook on the opportunities, challenges, and two-way empowerment of the laser and AI disciplines.

By analyzing published research articles indexed in the Web of Science, AI-assisted laser development can be divided into five parts: optimal design of laser components, optimal design of laser systems, intelligent control and optimization of laser beams, accurate characterization and prediction of laser properties, and optimization of laser application effectiveness. Regarding the optimal design of laser components, AI-assisted device design not only improves design efficiency but also allows better parameter optimization, which can aid optimization of laser systems and can play an important role in laser generation, transmission, and application. In terms of the optimal design of laser systems, AI can avoid complex physical principles for modeling and establish mapping between the laser performance and structural parameters, which accelerates the optimum design of the laser system for improved performance. Regarding intelligent control and optimization of laser beams, one example is the coherent beam combination (CBC). The control bandwidth is a bottleneck that limits implementation of large-scale CBC systems. An AI-assisted CBC system can overcome this limitation, and methods for coherently combining more than 100 beams are proposed. For the accurate characterization and prediction of laser properties, AI-enabled characterization technology can secure fast, accurate, and robust characterization of the mode content, beam quality, and pulse duration of lasers and shows great potential for the characterization of other properties of laser beams. In terms of the optimization of laser application effectiveness, AI can ignore the complex, highly nonlinear physical problems of light-matter interactions that occur during the laser-machining process and can achieve high-quality laser cutting/welding/additive manufacturing by establishing a mapping between the laser parameters and the processing quality.

In summary, AI technology is widely applied in laser research and applications. However, the rapid development of laser technology may also have a catalytic effect on the field of AI, ultimately creating a positive incentive for ‘two-way empowerment.'

AI-assisted lasers are expected to promote innovation and development of laser technology at the material, device, and system levels. At the material level, AI can help analyze and select laser materials by facilitating in-depth exploration of traditional optical fibers, semiconductors, and other materials, and expand the boundaries of the use of existing materials by improving their performance. At the device level, AI can revolutionize the design and development of laser-related devices. Data-driven modeling can provide theoretical analysis tools for complex laser phenomena and reveal the deeper physical mechanisms. Highly accurate device models can serve for the reverse design of specific device characteristics and enable multidimensional and comprehensive device function customization. At the system level, AI can provide efficient tools for the integration of laser systems, allowing for the simulation of operations during the system development phase, timely identification of potential problems, and efficient shaping and implementation of large and complex laser systems. AI-driven scientific research is a new frontier of AI, which has already been effective in several disciplines and is expected to inspire further breakthroughs in the future.

AI-assisted lasers not only result in breakthroughs in laser-performance indicators but also lead to breakthroughs in laser concepts, which may gradually become an enabling technology that is "used every day without realizing it" and can make future laser systems more attractive.

Furthermore, the development of laser technology can contribute to further development of AI by advancing arithmetic power. The current electronic computing performance relies on semiconductor lithography processes, and laser light sources are important factors supporting the continuous progress of lithography processes. In the future, laser-enabled ultra-computing photonic computers will hopefully drive AI technology.

AI-driven scientific research is a new frontier in AI worldwide and is effective in several disciplines, and the next five years are a critical window for its breakthrough development. In addition to the cross-fertilization of different disciplines in the scientific research process, the development of interdisciplinary and highly qualified personnel during the process of scientific research and education is a long-term strategy for the future.

Looking ahead, there are both opportunities and challenges; however, evolving AI technologies will certainly continue to facilitate the development of disciplines, such as lasers, and gradually build a new paradigm of basic and cutting-edge research supported by AI.

The existence of edge modes in the distributed Bragg reflector(DBR)semiconductor laser emission spectrum has a significant influence on the beam quality. The output multi-edge modes and high side lobes deteriorate the semiconductor laser beam and output power. The F-P effect formed on the sides of the DBR laser with a homogeneous duty cycle is one reason for this, resulting in side-mode resonance enhancement. Second, the energy coupled to the narrow-ridge waveguide through the grating decreases near the rim of the narrow ridge. This study proposes a modification scheme of the regular DBR structure to inhibit the edge mode on the DBR laser spectrum, ensuring maximized reflectance at the central wavelength while weakening the side lobe strength.

Based on coupled-mode theory, a rectangular grating model with a gradual duty cycle is established for the DBR laser. The influence of the duty cycle distribution on the edge mode suppression and the reflectivity maximization at the central wavelength is analyzed using the finite-difference time-domain (FDTD) method and changing the DBR duty cycle. The electro-optic model of the rectangular grating with gradient duty cycle is simulated, and the reflectance at the central wavelength and the side mode suppression ratio are obtained after device optimization. A tapered duty cycle model for the DBR is established, and the influence of the grating structure with tapered duty cycle on the output side lobe intensity of the DBR laser is analyzed.

In DBR lasers, the F-P effect exists in rectangular uniform duty cycle gratings, resulting in edge-mode oscillation enhancement (Fig. 5). From the relationship of coupling coefficient and duty cycle (Fig. 6) and the duty cycle under the maximum reflectivity at the central wavelength, the grating duty-cycle range can be determined. Within the gradient range, the simulation of grating when the duty cycle is truncated sinc function distribution is carried out. Within the gradient ranges of 0.545-0.580 and 0.580-0.545, the reflection intensity of the edge mode is suppressed, and the reflection peak value at the central wavelength reaches 0.8 (Fig.7). For a grating with length of 0.6 mm, comparing the reflection spectra of rectangular uniform grating and rectangular grating with gradient duty cycle, it can be observed that, although the edge mode is suppressed for the rectangular grating with gradient duty cycle, its reflection peak value at the central wavelength is lower than that of the rectangular uniform grating. Setting the duty cycle at 0.58 in the center of the grating, the edge mode suppressing effect is studied for the case when the duty cycle decreases from the center of the grating length to the two ends . The results show that the edge mode suppression under truncated sinc functions is enhanced, but the reflectivity at the central wavelength does not improve (Figs.9 and 10). When the grating length increases to 1 mm, the central reflection peak value of the grating with gradient duty cycle remains unchanged (Fig.11). When adopting the grating with gradient duty cycle, the coupling coefficient at the center of the grating length is small; thus, using the combination of duty cycle that presents truncated sinc function distribution and constant duty cycle can increase the coupling coefficient at the center of the grating. Although the reflection peak value at the central wavelength is nearly similar to that of the uniform grating, the edge mode suppression ratio reaches 48 dB (Fig.13). Finally, by observing the field intensity distribution of tapered grating with gradient duty cycle and rectangular uniform grating on the far-field lateral tangent, we find that tapered gratings with gradient duty cycle can reduce the sidelobe intensity of the DBR laser output.

The existence of the F-P effect in the rectangular uniform grating for conventional DBR lasers results in side-mode oscillation enhancement. In addition, more side modes in the reflectance spectra of semiconductor lasers not only deteriorate the beam quality but also reduce the output power at the central wavelength. It is found that changing the duty-ratio distribution in the DBR laser can destroy the grating effect, leading to a reduction in the side-mode reflection intensity. Furthermore, using a gradual duty cycle at both ends of the grating and a constant duty cycle in the center of the rectangular grating length, not only reduces the side mode in the reflection spectrum, but also ensures a larger reflectivity at the central wavelength. It is found that the optical field propagating at the edge of the narrow ridge waveguide, when passing through a tapered gratings with gradient duty cycle, can be better coupled into the narrow ridge waveguide, leading to an effective reduction in the intensity of the side lobe. Compared with previous schemes, this requires less complex process steps while enhancing the side mode suppressing ratio. Additionally, it can reduce the side lobe strength, providing a valuable reference for the structural design of high-power and high-beam-quality semiconductor lasers.

Optical vortices in the Laguerre-Gaussian (LG) mode that have a unique hollow intensity profile and non-zero orbital angular momentum are highly significant for various applications. The LG mode laser can be generated using external-cavity devices, such as holograms or cylindrical lens pairs, to transform a Hermite-Gaussian beam into a LG beam, or using the intracavity, where the intracavity components are utilized to preferentially oscillate the certain high-order modes within a laser resonator. In comparison to the external-cavity approaches, intracavity approaches typically yield superior power handling, beam quality, and conversion efficiency. However, there are very few demonstrations regarding high-order LG mode laser oscillations with angular indices (m) beyond 30. The main challenge is that the beam patterns of the very-high-order mode lasers become highly complex, which makes it difficult to fabricate mode-selecting elements with a sufficient accuracy to precisely manage the loss and gain of a certain mode. In this study, we demonstrate the generation of an ultra-high-order LG mode output based on mode selection enabled by intracavity spherical aberration (SA). By calculating the focusing behavior of the high-order LG mode beam and the SA of the intracavity lens, the relationship between the angular indices m of the high-order LG_0,±m vortex laser and the cavity parameters is determined. In the experiment, the ultra-high-order LG_0,±m vortex lasers with tunable angular indices m of up to 280 are obtained with an end-pumped Nd∶YVO₄ laser at a wavelength of 1064 nm, under an incident diode pump power of only 2.06 W.

The experimental arrangement of the laser that generates the ultra-high-order LG mode output is depicted in Fig. 1. Two lenses, L1 and L2, with focal lengths of f₁=150 mm and f₂=51.8 mm, respectively, are inserted into the cavity of an end-pumped Nd∶YVO₄ laser to collimate the beam and introduce SA for mode selection. The laser is pumped by a fiber-coupled diode laser at 878.6 nm, with a pump beam radius of approximately 120 μm at the input facet of the a-cut Nd∶YVO₄ crystal and a Rayleigh length of approximately 0.9 mm. The crystal is located near the total reflector M1, while the distances between the crystal and lens L1 (d₁) and between lenses L1 and L2 (d₂) are 155 mm and 20 mm, respectively. The plano-concave input mirror M1 with a small radius of curvature of 50 mm generates a small beam waist near it, enabling the beam to expand significantly when it reached the lenses, thus enhancing the SA and resultant mode selection capability. The output coupler M2 is a flat mirror with a transmittance of 10% at 1064 nm. The beam waist position of the LG beam behind the focusing lens L2 can be obtained using Eq. (1). Considering that the beam arriving at lens L2 is well-collimated by lens L1, the relationship can be simplified as indicated in Eq. (3). Because the output coupler M2 is a flat mirror, the oscillating beam should have its waist exactly on the mirror surface. The defocused modes (with the beam waist deviated from M2) suffered a loss larger than that of the "on-focus" mode with the beam waist on M2. The spherical lens with SA is used as L2, and the focal length is not a constant but varies with the incident beam height. Therefore, mode selection can be achieved by adjusting the location of M2 within a small range to have different orders of modes focused on it. Moving the output coupler M2 toward lens L2 will result in modes with a larger m and vice versa.

Figure 4 presents certain typical beam patterns recorded during the experiment. With an incident pump power of 2.06 W, the lowest order propagation-invariant single-mode LG_0,±m mode laser is LG_0,±38, which is obtained at distance between M2 and L2 of d₃=51.48 mm, and the highest order is LG_0,±280, which is obtained at d₃=48.91 mm. The beams are petal-like because both the +m and -m components have a similar intensity, and the mode order can be determined by counting the surrounding dark bars. The high-order LG mode optical vortices demonstrate good mode purity and stability. Figure 5 presents the theoretical relationship of the mode order and the d₃ obtained using Eq. (3), as well as the experimental results, which sufficiently match the theoretical results. The slope efficiency of the laser decreases with the mode order owing to the increasing SA-induced cavity loss and decreasing mode matching.

In summary, ultra-high-order LG mode vortex beams with selective angular indicesare obtained by utilizing the SA of an off-the-shelf spherical lens in the laser cavity. By calculating the focusing behavior of the high-order LG mode beam and the SA of the intracavity lens, the relationship between the angular indices m of the high-order LG_0,±m vortex laser and the cavity parameters is determined. In the experiment, an ultra-high-order LG_0,±m vortex laser with tunable angular indices m of up to 280 is obtained with an end-pumped Nd∶YVO₄ laser at a wavelength of 1064 nm, under an incident diode pump power of only 2.06 W. The ultra-high-order LG_0,±m vortex laser exhibits good stability in terms of power and the transverse mode. The mode evolution in the experiment sufficiently matches with that in the theoretical model. This study provides theoretical and experimental references for the generation of ultra-high-order LG mode vortex lasers. By increasing the pump power or pump overlap to enhance the laser gain, arbitrary high-order modes can be expected using this method.

The mid-infrared waveband is the vibrational and rotational spectral region of molecules, of which 3-5 μm is the most important atmospheric window, making it an increasingly popular research topic. The wide-tuning characteristics of the external cavity quantum cascade laser in the mid-infrared waveband make it widely used in gas molecule sensing, difference-frequency THz generation, free-space optical communication, and other fields. We design a tunable quantum cascade laser with a 4-μm wavelength to realize these applications. The laser can achieve different light emission performances by replacing blazed grating, making it suitable for different conditions.

The experiment in this study is performed with the Littrow structure as the main body and quantum cascade gain chip with a central wavelength of 4 μm. During the experiment, the working temperature of the gain chip is kept at 25 ℃, and the quantum cascade laser gain chip is integrated with the thermoelectric cooler and even aspherical lens. Blazed gratings with groove spacings of 450 line/mm and 300 line/mm are selected as the feedback elements, and the zero-order diffraction light of the grating is selected as the output light; the first-order diffraction light is fed back to the active region of the gain chip to form an external cavity resonance. The feedback light is returned to the laser active region and the laser wavelength is selected by adjusting the grating pitch angle and rotation angle.

Based on the above results, the laser maximum power and spectral tuning range are 7.30 mW (Fig. 8) and 380 nm and the grating rotational angle is 11.06° when 450 line/mm gratings are used. With this configuration, the laser has a higher power value and wider spectral tuning characteristics, which is more suitable for applications requiring narrow linewidth and high-precision wavelength tuning, such as spectroscopy. When a 300 line/mm blazed grating is used, the highest power is 5.24 mW (Fig. 8), spectral tuning range is 297 nm (Fig. 5), and the rotation angle of the grating is 3.15°. This configuration is more suitable for space optical communication and other applications requiring high beam quality. The laser side-mode suppression ratio (SMSR) in both configurations is 20 dB (Fig. 6), which is suitable for practical use.

In this study, a widely tunable external cavity quantum cascade laser based on the Littrow structure is developed. A blazed grating is used as the feedback element for mode selection, and two gratings with different grating constants are selected for comparison. Experimental comparisons show that when a 450 line/mm blazed grating is used, a maximum power value of 7.30 W, tuning range of 3774-4154 nm, tuning width of 380 nm, and grating rotation angle of 11.06° are obtained. The laser has a higher power value and a wider spectral tuning range. When a 300 line/mm blazed grating is used, the laser beam quality is improved, with a maximum power value of 5.24 mW, tuning range of 3779-4154 nm, tuning width of 297 nm, and grating rotation angle of 3.15°. The 300 line/mm blazed grating configuration is more suitable for high beam quality applications, such as space optical communication. The performance of the laser obtained by using the 450 line/mm blazed grating configuration is more suitable for spectral applications requiring narrow linewidth and high-precision spectral tuning. An external cavity quantum cascade laser can achieve different performance indices by using different external cavity configurations and meet the use requirements of different application scenarios. It plays an important role in molecular gas sensing, difference-frequency terahertz generation, free-space optical communication, and other fields.