Congratulations to our colleague MSc. Duong Ngoc Bao Trung and other student, for them recent publication entitled "Light-modulated synaptic response of planar WS2–PVA hybrid memristors for artificial synapses and quantization-aware neural networks" in the journal "Materials Science in Semiconductor Processing", which was a collaboration with our colleagues in the Surface Science Laboratory, Toyota Technological Instituate, Nagoya, Japan and VNU - Hanoi University of Science,
This study presents two-dimensional material-based artificial synapses that combine stable electrical switching with optical tunability, which are promising for neuromorphic systems. Here, a planar Cr/WS2–PVA/Cr memristive device was fabricated by embedding liquid-phase-exfoliated WS2 sheets in a PVA matrix. The device exhibited stable analog bipolar resistive switching with self-rectifying behavior over 300 cycles. Its switching characteristics were associated with defect-assisted charge trapping, Poole–Frenkel-type emission, and space-charge-assisted transport within the WS2–PVA hybrid active layer. Sulfur-vacancy-related states and polymer-assisted trapping centers contributed to the gradual modulation of conductance. Under 425 nm illumination, extending the exposure time from 30 to 120 s increased the conductance from 1.7 to 5.4 μS, facilitating light-assisted learning and relaxation-based forgetting behavior after light removal. Hardware-aware ResNet-20 simulations using the measured conductance states achieved a CIFAR-10 accuracy of 81.91 ± 0.29% after 120 s of illumination, averaged over five independent seeds. The device-aware models maintained classification accuracies above 81% under both dark and illuminated conditions, demonstrating the feasibility of integrating experimentally obtained WS2–PVA conductance states into neuromorphic inference. These findings highlight the potential of WS2–PVA hybrid memristors for electrically and optically tunable artificial synapses and neuromorphic inference..
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Acknowledgment
This research is funded by the Vietnam National University – Hanoi under grant number QG.24.98.
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