Optoelectronic stimuli-driven switchable memristors with multilevel resistance states for neuromorphic vision sensors

Advancements in next-generation artificial intelligence technology have revolutionized neuromorphic visual systems by surpassing the limitations of the visible light range. However, the intricate circuitry of artificial visual systems, which relies on traditional image sensors, processing units, and memory, poses significant obstacles to device computational speed, large-scale integration, and power consumption. In this research, synaptic devices were developed with novel optoelectronic two-terminal resistive random-access memory. In this manner, a robust neuromorphic visual system with synaptic behaviors and nonvolatile optical resistive switching achieved by optical stimuli can be developed. The proposed optoelectronic synapse exhibits a symmetric and linear conductance-update trajectory with several conductance states, enabling image-sensing memory functions and efficient neuromorphic visual image pattern recognition with robust fault resistance. The optoelectronic memristor logic function "OR" can also boost the information-sensing potential of neuromorphic visual sensors. This work aimed to advance the creation of cutting-edge optogenetic neuromorphic vision systems. The developed optoelectronic synaptic devices with resistive switching enable efficient, fault-resistant image recognition. The PMMA/ZnO/2IF memristor array advances neuromorphic vision systems, offering high accuracy and low power consumption.