Alagappan, G., Ong, J. R., Yang, Z., Ang, T. Y. L., Zhao, W., Jiang, Y., Zhang, W., & Png, C. E. (2022). Leveraging AI in Photonics and Beyond. Photonics, 9(2), 75. https://doi.org/10.3390/photonics9020075
Abstract:
Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - AI-Enabled Electronic-Photonic IC Design
Grant Reference no. : RGANS1901
This research / project is supported by the A*STAR - RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund
Grant Reference no. : A20H5b0142