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用于相干光学神经网络中可重构相位相关激活函数的石墨烯/硅异质结

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

作者信息

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

机构信息

State Key Laboratory of Modern Optical Instrumentation, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.

State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, 100871, Beijing, China.

出版信息

Nat Commun. 2023 Oct 31;14(1):6939. doi: 10.1038/s41467-023-42116-6.

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.

摘要

光学神经网络(ONNs)开创了信息与通信技术的新纪元,并已实现了各种智能应用。在光学神经网络中,激活函数(AF)是决定网络性能的关键组件,而片上激活函数器件仍在研发之中。在此,我们首次展示了通过双功能石墨烯/硅(Gra/Si)异质结实现相位激活的片上可重构激活函数器件。由于在一个器件中实现了光调制和检测,与其他片上激活函数策略相比,其时间延迟更短、能耗更低、可重构性更高且器件占地面积更小。我们的Gra/Si异质结的实验调制电压(功率)低至1 V(0.5 mW),优于许多纯硅同类器件。在光电探测方面,实现了超过200 mA/W的高响应度。所产生的特殊非线性函数被输入到一个复值光学神经网络中,以挑战手写字母和图像识别任务,展现出更高的准确性以及高效、全组件集成片上光学神经网络的潜力。我们的研究结果为片上光学神经网络器件提供了新的见解,并为高性能集成光电计算电路铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bef/10618201/1695b6bd6bff/41467_2023_42116_Fig1_HTML.jpg

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