| Autor: |
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 |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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