Volume: 3 Issue 4
4 Article(s)

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Recent advances in photonics of three-dimensional Dirac semimetal Cd3As2
Renlong Zhou, Kaleem Ullah, Naveed Hussain, Mohammed M. Fadhali, Sa Yang, Qiawu Lin, Muhammad Zubair, and Muhammad Faisal Iqbal
Due to their unusual features in condensed matter physics and their applicability in optical and optoelectronic applications, three-dimensional Dirac semimetals (3D DSMs) have garnered substantial interest in recent years. In contrast to monolayer graphene, 3D DSM exhibits linear band dispersion despite its macroscopic thickness. Therefore, being a bulk material, it is easy to make nanostructures with 3D DSM, just as one normally does with metals such as gold and silver. Among 3D DSMs, cadmium arsenide (Cd3As2) is quite famous and considered an excellent 3D DSM due to its chemical stability in air and extraordinary optical response. In this review, advances in 3D DSM Cd3As2 fabrication techniques and recent progress in the photonics of 3D DSM Cd3As2 are given and briefly reviewed. Various photonic features, including linear and nonlinear plasmonics, optical absorption, optical harmonic generation, and ultrafast dynamics, have been explored in detail. It is expected that Cd3As2 would share an excellent tunable photonic response like graphene. We envision that this article may serve as a concise overview of the recent progress of photonics in 3D DSM Cd3As2 and provides a compact reference for young researchers.
Review of Optics: a virtual journal
  • Publication Date: Nov. 14, 2022
  • Vol. 1 Issue 2 024001 (2022)
Deep learning spatial phase unwrapping: a comparative review | Article Video
Kaiqiang Wang, Qian Kemao, Jianglei Di, and Jianlin Zhao

Phase unwrapping is an indispensable step for many optical imaging and metrology techniques. The rapid development of deep learning has brought ideas to phase unwrapping. In the past four years, various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly. However, these methods were proposed and analyzed individually, using different strategies, neural networks, and datasets, and applied to different scenarios. It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context. We first divide the phase dataset generation methods into random matrix enlargement, Gauss matrix superposition, and Zernike polynomials superposition, and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression, deep-learning-performed wrap count, and deep-learning-assisted denoising. For the phase dataset generation methods, the richness of the datasets and the generalization capabilities of the trained networks are compared in detail. In addition, the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal, noisy, discontinuous, and aliasing cases. Finally, we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method, neural network structure, generalization ability enhancement, and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods.

Review of Optics: a virtual journal
  • Publication Date: Aug. 03, 2022
  • Vol. 1 Issue 1 014001 (2022)
Silicon nitride-based Kerr frequency combs and applications in metrology
Zhaoyang Sun, Yang Li, Benfeng Bai, Zhendong Zhu, and Hongbo Sun
Kerr frequency combs have been attracting significant interest due to their rich physics and broad applications in metrology, microwave photonics, and telecommunications. In this review, we first introduce the fundamental physics, master equations, simulation methods, and dynamic process of Kerr frequency combs. We then analyze the most promising material platform for realizing Kerr frequency combs—silicon nitride on insulator (SNOI) in comparison with other material platforms. Moreover, we discuss the fabrication methods, process optimization as well as tuning and measurement schemes of SNOI-based Kerr frequency combs. Furthermore, we highlight several emerging applications of Kerr frequency combs in metrology, including spectroscopy, ranging, and timing. Finally, we summarize this review and envision the future development of chip-scale Kerr frequency combs from the viewpoint of theory, material platforms, and tuning methods.
Review of Optics: a virtual journal
  • Publication Date: Nov. 14, 2022
  • Vol. 4 Issue 6 064001 (2022)
Computation at the speed of light: metamaterials for all-optical calculations and neural networks
Trevon Badloe, Seokho Lee, and Junsuk Rho
The explosion in the amount of information that is being processed is prompting the need for new computing systems beyond existing electronic computers. Photonic computing is emerging as an attractive alternative due to performing calculations at the speed of light, the change for massive parallelism, and also extremely low energy consumption. We review the physical implementation of basic optical calculations, such as differentiation and integration, using metamaterials, and introduce the realization of all-optical artificial neural networks. We start with concise introductions of the mathematical principles behind such optical computation methods and present the advantages, current problems that need to be overcome, and the potential future directions in the field. We expect that our review will be useful for both novice and experienced researchers in the field of all-optical computing platforms using metamaterials.
Review of Optics: a virtual journal
  • Publication Date: Dec. 21, 2022
  • Vol. 4 Issue 6 064002 (2022)