Optoelectrical Conditioning in Graphene Quantum Dot–Vanadium Oxide Artificial Synapse

Neuromorphic computing requires artificial synaptic devices capable of adaptive learning and memory retention. Here, we present a monolithically integrated optoelectronic synaptic device based on a 0D/3D Ag/graphene quantum dot–vanadium oxide (GQD–VO 2 )/Ni heterojunction exhibiting stimulation‐history‐dependent conductance modulation. The device supports synaptic modulation under electrical, optical, and combined optoelectrical stimulation, with enhanced optoelectronic response compared with the bare VO 2 device. Repeated optoelectrical stimulation modifies the retained device state and subsequent potentiation–depression characteristics, a process described here as optoelectrical conditioning. This evolution is evaluated using retained current ( I ), fitted potentiation–depression time constants ( τ ), and nonlinearity coefficients. Wavelength‐dependent optical associative learning with positive and negative reinforcement is further demonstrated under different optical stimuli. Performance evaluation using Fashion‐MNIST and Digit‐MNIST datasets show improved classification accuracy with the progression of optoelectrical conditioning learning cycles, increasing from 81.53% to 84.13% and from 91.71% to 95.58%, respectively. Monte Carlo simulations further indicate improved classification accuracy across multiple runs following repeated learning cycles. Furthermore, this behavior is illustrated through reconstructed Fashion‐MNIST image outputs with the repeated learning cycles. These findings highlight the potential of Ag/GQD–VO 2 /Ni device for neuromorphic applications, advancing AI‐integrated bio‐inspired computing.