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

Chuyu Zhong, Kun Liao, Tianxiang Dai, Maoliang Wei, Hui Ma, Jianghong Wu, Zhibin Zhang, Yuting Ye, Ye Luo, Zequn Chen, Jialing Jian, Chunlei Sun, Bo Tang, Peng Zhang, Ruonan Liu, Junying Li, Jianyi Yang, Lan Li, Kaihui Liu, Xiaoyong Hu () and Hongtao Lin ()
Additional contact information
Chuyu Zhong: Zhejiang University
Kun Liao: Peking University
Tianxiang Dai: Peking University
Maoliang Wei: Zhejiang University
Hui Ma: Zhejiang University
Jianghong Wu: Westlake University
Zhibin Zhang: Peking University
Yuting Ye: Westlake University
Ye Luo: Westlake University
Zequn Chen: Westlake University
Jialing Jian: Westlake University
Chunlei Sun: Westlake University
Bo Tang: Institute of Microelectronics of the Chinese Academy of Sciences
Peng Zhang: Institute of Microelectronics of the Chinese Academy of Sciences
Ruonan Liu: Institute of Microelectronics of the Chinese Academy of Sciences
Junying Li: Zhejiang University
Jianyi Yang: Zhejiang University
Lan Li: Westlake University
Kaihui Liu: Peking University
Xiaoyong Hu: Peking University
Hongtao Lin: Zhejiang University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42116-6

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