Optoelectronic dual-synapse based on wafer-level GaN-on-Si device incorporating embedded SiO2 barrier layers

Abstract

Optoelectronic synapses possess the potential to seamlessly integrate perception, storage, and neuromorphic computation, thereby offering advantages in constructing artificial vision systems. Gallium nitride (GaN) materials, renowned for their exceptional optoelectronic characteristics and the compatibility of Silicon-based GaN (GaN-on-Si) with CMOS technology, contribute significantly to realizing large-scale optoelectronic neuromorphic circuits. In this study, we present an optoelectronic dual-synapse based on a structurally-improved AlGaN/GaN MIS-HEMT with ring-shaped SiO2 barriers embedded surrounding the source and drain regions. This device facilitates simultaneous signal transmission to the source and drain through current-limiting electrical breakdown beneath the gate. This integration of two discrete optoelectronic synapses within a single device enhances neural signal transmission nodes and branching connectivity, facilitating the construction of large-scale neuromorphic circuits. Under the co-influence of electrical and optical pulses, the device not only demonstrates excellent optoelectronic synaptic characteristics but also exhibits the ability to modulate the critical wavelength of synaptic responses by varying the source/drain voltage, effectively simulating the light signal recognition observed in biological retinas. The synergistic operation of the two sub-synapses enables a successful transition from short-term potentiation (STP) to long-term potentiation (LTP). Furthermore, leveraging the device's STP, we emulate the function of artificial retina through a GaN optoelectronic neuromorphic array, successfully achieving image recognition in conjunction with artificial neural networks (ANNs). The devices developed in this study, based on GaN-on-Si allow wafer-level manufacturing, provding a promising avenue for the large-scale production of optoelectronic synapse devices and the realization of artificial vision systems.