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

TL;DR

This paper introduces reconfigurable phase-relevant activation functions in optical neural networks using graphene/silicon heterojunctions, enabling more efficient, compact, and high-performance on-chip optoelectronic computing.

Contribution

The study demonstrates a novel on-chip reconfigurable activation device with phase modulation using graphene/silicon heterojunctions, outperforming existing silicon-based strategies in efficiency and integration.

Findings

Achieved low modulation voltage of 1 V and power of 0.5 mW.

Realized high photodetection responsivity over 200 mA/W.

Improved accuracy in image recognition tasks using the new activation functions.

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.