Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir

A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in-sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self-powered Au/P(VDF-TrFE)/Cs2AgBiBr6/ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in-sensor RC system takes advantage of near-zero energy consumption in the reservoir, resulting in decades-time lower training costs than a conventional neural network. This work paves the way for ultralow-power machine vision using photonic devices.

Automated summary

A neuromorphic visual system integrating optoelectronic synapses and self-powered devices achieves high energy efficiency and accuracy, providing a new direction for ultralow-power machine vision.