Atomically Thin Synaptic Devices for Optoelectronic Neuromorphic Vision

Imaging sensors with inbuilt processing capability are expected to form the backbone of low-latency and highly energy efficient artificial vision systems. A range of emerging atomically thin materials provide opportunities to exploit their electrical and optical properties for human vision and brain inspired functions. This work reports atomically thin nanosheets of beta-In2S3 which exhibit inherent persistent photoconductivity (PPC) under ultraviolet and visible wavelengths. This PPC effect enables beta-In2S3-based optoelectronic devices to optically mimic the dynamics of biological synapses. Based on the material characterizations, the PPC effect is attributed to the intrinsic defects in the synthesized beta-In2S3 nanosheet. Furthermore, the feasibility of adopting these atomically thin synaptic devices for optoelectronic neuromorphic hardware is demonstrated by implementing a convolutional neural network for image classification. As such, the demonstrated atomically thin nanosheets and optoelectronic synaptic devices provide a platform for scaling up complex vision-sensory neural networks, which can find many promising applications for multispectral imaging and neuromorphic computation.

Automated summary

This study reports the use of atomically thin nanosheets of beta-In2S3 to mimic the dynamics of biological synapses for optoelectronic devices. The persistent photoconductivity (PPC) effect exhibited by these nanosheets enables them to function as synaptic devices. These atomically thin nanosheets have the potential for applications in multispectral imaging and neuromorphic computation.