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Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks

Authors :
Zhong, Chuyu
Liao, Kun
Dai, Tianxiang
Wei, Maoliang
Ma, Hui
Wu, Jianghong
Zhang, Zhibin
Ye, Yuting
Luo, Ye
Chen, Zequn
Jian, Jialing
Sun, Chulei
Tang, Bo
Zhang, Peng
Liu, Ruonan
Li, Junying
Yang, Jianyi
Li, Lan
Liu, Kaihui
Hu, Xiaoyong
Lin, Hongtao
Publication Year :
2023

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2307.06882
Document Type :
Working Paper