A 2T2R1C vision cell with 140 dB dynamic range and event-driven characteristics for in-sensor spiking neural network

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 2 phototransistors exhibits inherently high dynamic range (140 dB), thus greatly simplifying event-driven sensor circuit design. The photoresponvity of MoS 2 phototransistors ranges from 1$0^{-4}$ to 10 ⁴ 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.