Recently, research interest in brain-inspired neuromorphic computing based on robust and intelligent artificial neural networks has surged owing to the ability of such technology to facilitate massive parallel, low-power, highly adaptive, and event-driven computing. Here, a photosynaptic device with a novel weight updating mechanism for high-speed and low-power optoelectronic spike processing is proposed, wherein a synaptic weight is controlled by a mixed spike consisting of voltage and light spikes; the light spike, in particular, boosts up the probability of electron detrapping from graphene oxide charge-trapping layer to the photosensitive indium–gallium–zinc oxide charge-transporting layer. Compared to electrically operating synaptic device, the magnitude of conductance change in the proposed photosynaptic device increases remarkably from 2.32 to 5.95 nS without degradation of the nonlinearity (potentiation/depression values are changed from 4.24/8 to 5/8). Owing to this enhancement of synaptic operation, the recognition rates for the Modified National Institute of Standards and Technology digit patterns improve from 36% and 49% to 50% and 62% in artificial neural networks using long-term potentiation/depression characteristics with 20 and 100 weight states, respectively. The proposed photosynaptic device technology capable of optoelectronic spike processing is expected to play a crucial role in the implementation of neuromorphic computing in the future.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics