Light-modulated synaptic response of planar WS2–PVA hybrid memristors for artificial synapses and quantization-aware neural networks

Two-dimensional-material-based artificial synapses that combine stable electrical switching with optical tunability are promising for neuromorphic systems. Here, a planar Cr/WS 2 –PVA/Cr memristive device was fabricated using liquid-phase-exfoliated WS 2 sheets embedded 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 WS 2 –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 WS 2 –PVA conductance states into neuromorphic inference. These findings highlight the potential of WS 2 –PVA hybrid memristors for electrically and optically tunable artificial synapses and neuromorphic inference.

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Publication Details

Journal
Materials Science in Semiconductor Processing
Published
2026-09-29
DOI
https://doi.org/10.1016/j.mssp.2026.111223
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Light-modulated synaptic response of planar WS2–PVA hybrid memristors for artificial synapses and quantization-aware neural networks

Thuat Nguyen-Tran, Ngoc Kim Pham, D. Son, Phu‐Quan Pham et al.
Materials Science in Semiconductor Processing
Advanced Memory and Neural Computing
article

Light-modulated synaptic response of planar WS2–PVA hybrid memristors for artificial synapses and quantization-aware neural networks

Thuat Nguyen-Tran, Ngoc Kim Pham, D. Son, Phu‐Quan Pham, Nhat Quang Minh Tran, Thuy Thi Dieu Ung, Quang Nguyen, Trung Bao Ngoc Duong, Duc Minh Nguyen, Hoang-Tho Nguyen, Hoang-Tuan Tong, Masamichi Yoshimura, Huong Ngoc Phan, Truong Phi Le
article en

Abstract

Two-dimensional-material-based artificial synapses that combine stable electrical switching with optical tunability are promising for neuromorphic systems. Here, a planar Cr/WS 2 –PVA/Cr memristive device was fabricated using liquid-phase-exfoliated WS 2 sheets embedded 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 WS 2 –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 WS 2 –PVA conductance states into neuromorphic inference. These findings highlight the potential of WS 2 –PVA hybrid memristors for electrically and optically tunable artificial synapses and neuromorphic inference.

Materials Science in Semiconductor ProcessingVol. 218
Vietnam National University Ho Chi Minh City (VN), Vietnam National University, Hanoi (VN), Ho Chi Minh City University of Science (VN), Institute of Materials Science (VN), Ho Chi Minh City University of Technology (VN), Toyota Technological Institute (JP), VNU University of Science (VN), Vietnam Academy of Science and Technology (VN)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
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