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Title: Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
Authors: Zefeng Xu
Baoshan Tang 
Jieming Pan 
Sonu Hooda
Evgeny Zamburg 
Voon Yew Thean 
Issue Date: 6-Sep-2022
Publisher: Light: Science & Applications
Citation: Zefeng Xu, Baoshan Tang, ZHANG XIANGYU, LEONG JIN FENG, Jieming Pan, Sonu Hooda, Evgeny Zamburg, Voon Yew Thean (2022-09-06). Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch 11 (1). ScholarBank@NUS Repository.
Rights: CC0 1.0 Universal
Abstract: Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vectormatrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient insitu nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS2 Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).
ISSN: 2095-5545
DOI: 10.1038/s41377-022-00976-5
Rights: CC0 1.0 Universal
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