2023/07/13 by Chuyu Zhong, Kun Liao, Zhong, Chuyu +39 · 1 citation
Computer Science · Engineering · #Applied Physics (physics.app-ph) #FOS: Physical sciences #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics (physics.optics) #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.2307.06882
openalex publication_date 2023/07/13 · openalex created_date 2023/07/15 · openalex updated_date 2026/07/28
Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.