Author(s): Huang, Y (Huang, Ying); Wang, WP (Wang, Weiping); Qiao, L (Qiao, Lei); Hu, XY (Hu, Xiaoyan); Chu, T (Chu, Tao)

Source: OPTICS LETTERS Volume: 47 Issue: 7 Pages: 1810-1813 DOI: 10.1364/OL.451287 Published: APR 1 2022

Abstract: We experimentally demonstrate two types of programmable, low-threshold, optically controlled nonlinear activation functions, which are challenging to realize in photonic neural networks (PNNs). These devices rely on on-chip integrated Ge-Si photoelectric detectors and silicon electro-optical switches, and they generate rectified linear unit (ReLU) or sigmoid functions with arbitrary slopes without additional electrical processing. Both devices function at an extremely low threshold of 0.2mW. The embedding of these nonlinear activation functions into convolutional neural networks facilitates the attainment of high inference accuracies of up to 95% when applied to Modified National Institute of Standards and Technology (MNIST) handwritten digit-classification tasks. The devices are suitable for low-power PNNs with an arbitrary number of propagation layers in photonic-computing chips. (C) 2022 Optica Publishing Group

Accession Number: WOS:000778798800061

PubMed ID: 35363741

ISSN: 0146-9592

eISSN: 1539-4794

Full Text: https://opg.optica.org/ol/fulltext.cfm?uri=ol-47-7-1810&id=470893