A 2T2R1C vision cell with 140 dB dynamic range and event-driven characteristics for in-sensor spiking neural network
Yue Zhou, Jiawei Fu, Tianqing Wan, Lin Xu, Sijie Ma, Jiewei Chen, Xiangshui Miao, Yuhui He, Yang Chai
Abstract
To efficiently process vision data at sensor terminals, we demonstrate a 2T2RlC pixel cell for in-sensor spike neural network (SNN) that can sense and process vision informations with event-driven characteristics. Compared with conventional event-based cameras with Si photodiode that requires complicated CMOS circuit design for high dynamic range (120dB), our2T2RlC cell with two-dimensioinal MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> phototransistors exhibits inherently high dynamic range (140 dB), thus greatly simplifying event-driven sensor circuit design. The photoresponvity of MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> phototransistors ranges from 1$0^{-4}$ to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> mA/W by modulating gate voltages, emulating the synaptic weights in a neural network. Based on this sensor, we construct an in-sensor SNN and successfully perform a lane keeping task in an event-driven manner. Instead of sensing and generating the spike signals of all pixels, our design saves 97% data by only processing the local pixel with the change of light intensity. When an event is triggered (light intensity changes from 1.6 to 5.1 mWc$\mathrm{m}^{-2}$), each pixel realizes event-based sensing and processing simultaneously with ultralow power consumption (160nW), showing the potential for energy-efficient edge intelligence.