RGB Color-Discriminable Photonic Synapse for Neuromorphic Vision System

초록

Photonic synapse capable of multispectral color discrimination is demonstrated.Strong excited-state dipoles enable remarkable discrimination intensity (0.05-40 mW cm-2).This approach is not restricted to a specific medium in the channel layer, and convolutional neural network with synapses array achieves over 94% inference accuracy for Canadian-Institute-For-Advanced-Research-10 images. To emulate the functionality of the human retina and achieve a neuromorphic visual system, the development of a photonic synapse capable of multispectral color discrimination is of paramount importance. However, attaining robust color discrimination across a wide intensity range, even irrespective of medium limitations in the channel layer, poses a significant challenge. Here, we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities. By incorporating the strong induced dipole moment effect at the excitation, modulated by the wavelength of the incident light, into the floating gate, we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm-2. This approach is not restricted to a specific medium in the channel layer, thereby enhancing its applicability. The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array, involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction. Consequently, a convolutional neural network can achieve an impressive inference accuracy of over 94% for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing. Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses, enabling significant advancements in artificial visual systems.

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