(Peer-Reviewed) Reconfigurable optical neural networks with Plug-and-Play metasurfaces
Yongmin Liu ¹ ², Yuxiao Li ²
¹ Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
² Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA
Opto-Electronic Advances, 2024-06-04
Abstract
In a very recent study, Prof. Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces. By fixing one metasurface and switching the other pluggable metasurfaces, the neural networks, which operate at near-infrared wavelengths, can perform distinct recognition tasks for handwritten digits and fashion products. This innovative device opens up an avenue for all-optical, high-speed, low-power, and multi-functional artificial intelligence systems.
Flicker minimization in power-saving displays enabled by measurement of difference in flexoelectric coefficients and displacement-current in positive dielectric anisotropy liquid crystals
Junho Jung, HaYoung Jung, GyuRi Choi, HanByeol Park, Sun-Mi Park, Ki-Sun Kwon, Heui-Seok Jin, Dong-Jin Lee, Hoon Jeong, JeongKi Park, Byeong Koo Kim, Seung Hee Lee, MinSu Kim
Opto-Electronic Advances
2025-09-25
Dual-frequency angular-multiplexed fringe projection profilometry with deep learning: breaking hardware limits for ultra-high-speed 3D imaging
Wenwu Chen, Yifan Liu, Shijie Feng, Wei Yin, Jiaming Qian, Yixuan Li, Hang Zhang, Maciej Trusiak, Malgorzata Kujawinska, Qian Chen, Chao Zuo
Opto-Electronic Advances
2025-09-25
