A multifunctional optoelectronic synaptic device for logic operation and neuromorphic computing

The evolution of energy-efficient neuromorphic hardware capable of mimicking biological synaptic functions is critical for next-generation artificial intelligence and edge-computing systems. However, the integration of optoelectronic synaptic functionality with chemically doped two-dimensional (2D) tungsten diselenide (WSe 2 ) remains insufficiently explored. Here, we report an optically driven neuromorphic memristor based on a tri-layer WSe 2 with engineered carrier polarity via benzyl viologen (BV) doping. The structural integrity and material quality were verified using atomic force microscopy (AFM) and Raman spectroscopy, while electrical characteristics are systematically analyzed using gate-dependent transfer curves ( I ds -V g ) and current-voltage measurements ( I ds -V ds ), supported by an energy band diagram illustrating the Fermi level shift after doping. The BV-doped WSe 2 devices demonstrate pronounced photoresponses under visible and deep-ultraviolet (DUV) illumination, with DUV-induced charge trapping enabling stable memory retention. Optical-pulse stimulation produces tunable conductance modulation, including paired-pulse facilitation (PPF) indicative of short-term plasticity, as well as long-term potentiation and depression. By controlling the DUV pulse duration, the BV-doped WSe 2 device response is increased from 25.2% to 1438.7%. The device further enables selective alphabet recognition and logic OR gate operation. Importantly, synaptic weight characteristics are implemented in an artificial neural network trained to identify handwritten digits on the MNIST dataset with an accuracy of 92%, validated through epoch-dependent learning and confusion matrix analysis. This work may provide a promising platform for low-power, optically programmable neuromorphic systems for intelligent sensing technologies.

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Publication Details

Journal
Advanced Composites and Hybrid Materials
Published
2026-10-01
DOI
https://doi.org/10.1007/s42114-026-02103-z
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

A multifunctional optoelectronic synaptic device for logic operation and neuromorphic computing

Rana Faryad Ali, Iqra Rabani, Ghulam Dastgeer, Sobia Nisar et al.
Advanced Composites and Hybrid Materials
Advanced Memory and Neural Computing
article

A multifunctional optoelectronic synaptic device for logic operation and neuromorphic computing

Rana Faryad Ali, Iqra Rabani, Ghulam Dastgeer, Sobia Nisar, Ali Alsalme, Jonghwa Eom, Kashif Hussain, Inshaal Fatima
article en

Abstract

The evolution of energy-efficient neuromorphic hardware capable of mimicking biological synaptic functions is critical for next-generation artificial intelligence and edge-computing systems. However, the integration of optoelectronic synaptic functionality with chemically doped two-dimensional (2D) tungsten diselenide (WSe 2 ) remains insufficiently explored. Here, we report an optically driven neuromorphic memristor based on a tri-layer WSe 2 with engineered carrier polarity via benzyl viologen (BV) doping. The structural integrity and material quality were verified using atomic force microscopy (AFM) and Raman spectroscopy, while electrical characteristics are systematically analyzed using gate-dependent transfer curves ( I ds -V g ) and current-voltage measurements ( I ds -V ds ), supported by an energy band diagram illustrating the Fermi level shift after doping. The BV-doped WSe 2 devices demonstrate pronounced photoresponses under visible and deep-ultraviolet (DUV) illumination, with DUV-induced charge trapping enabling stable memory retention. Optical-pulse stimulation produces tunable conductance modulation, including paired-pulse facilitation (PPF) indicative of short-term plasticity, as well as long-term potentiation and depression. By controlling the DUV pulse duration, the BV-doped WSe 2 device response is increased from 25.2% to 1438.7%. The device further enables selective alphabet recognition and logic OR gate operation. Importantly, synaptic weight characteristics are implemented in an artificial neural network trained to identify handwritten digits on the MNIST dataset with an accuracy of 92%, validated through epoch-dependent learning and confusion matrix analysis. This work may provide a promising platform for low-power, optically programmable neuromorphic systems for intelligent sensing technologies.

Advanced Composites and Hybrid Materials
University of Antwerp (BE), Government College University, Faisalabad (PK), Peking University (CN), Dongguk University (KR), King Saud University (SA), Sejong University (KR), Massachusetts Institute of Technology (US)
Affordable and clean energy
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
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