期刊
ADVANCED SCIENCE
卷 9, 期 15, 页码 -出版社
WILEY
DOI: 10.1002/advs.202106092
关键词
Cs2AgBiBr6; in-sensors; lead-free double perovskites; machine vision; reservoir
资金
- NSFC [62174053, 61804055, 62004204]
- Shanghai Science and Technology Innovation Action Plan [19JC141670, 21JC1402000, 21520714100]
- Open Research Projects of Zhejiang Lab [2021MD0AB03]
- Natural Science Foundation of Shanghai [18ZR1410900]
- Fundamental Research Funds for the Central Universities
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.
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.
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