Atomically Thin Synaptic Devices for Optoelectronic Neuromorphic Vision
Taimur Ahmed, Azmira Jannat, Vaishnavi Krishnamurthi, Thiha Aung, Aishani Mazumder, Ali Zavabeti, Nitu Syed, Torben Daeneke, Jian Zhen Ou, Akram Al‐Hourani, Sumeet Walia
Abstract
Abstract 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 β‐In 2 S 3 which exhibit inherent persistent photoconductivity (PPC) under ultraviolet and visible wavelengths. This PPC effect enables β‐In 2 S 3 ‐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 β‐In 2 S 3 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.