All inorganic perovskite-based artificial synaptic device for self-optimized neuromorphic computing

Abstract

Artificial synapse that can mimic physiological synaptic behaviors has attracted extensive attentions in intelligent robots. However, it is an extreme challenge for artificial synapses to achieve self-optimized feedback of mimicking biological behavior. Herein, a novel self-powered artificial neural pathway (SANP) is developed by coupling CsPbBr_xI_(3-x)-based artificial synaptic device and triboelectric nanogenerator (TENG) for self-optimized neuromorphic computing. The TENG can convert external mechanical stimulation into electricity that acts not only as a supply source to power the SANP but also as electrical stimulation to transmit to the synaptic device for neuromorphic computing. The synaptic device’s conductance can be well modulated by the electrical stimulation, which tunes the height of Schottky barrier between Ag and CsPbBr_xI_(3-x), to simulate the regulation of synaptic plasticity. Simultaneously, the synaptic device can implement synaptic functions of learning and memory. Furthermore, the SANP as self-powered mechano-nociceptor can successfully mimic the nociceptor features of “threshold”, “relaxation” and “allodynia”. More importantly, under repeated mechanical stimulation, the SANP with synaptic self-optimized feedback features enables the learning and memory training and the robotic arm’s grabbing and spreading simultaneously. Consequently, the SANP can effectively accomplish signal transmission, processing, and learning tasks without external power supply, which demonstrates potential application in neuromorphic computing and intelligent robots.