Superior performance of printed optoelectronic synapses based on defect-controlled monolayer MoS <sub>2</sub> with ultralow power consumption for neuromorphic computing

Optoelectronic synapses (OES), which integrate photodetection and synaptic functions in a single platform, offer a promising approach to mimic the visual processing capabilities of the human brain. Two-dimensional (2D) materials are attractive for OES devices due to their excellent energy efficiency and high photoelectric conversion capability. However, most 2D material-based OES devices rely on conventional lithography and post-growth defect engineering, both of which require expensive cleanroom facilities and complex processing steps. Here, we present a hybrid fabrication strategy that integrates cost-effective printing technology, as an alternative to conventional lithography, with <i>in situ</i> defect engineering during CVD growth of monolayer MoS<sub>2</sub>. The latter intrinsically tunes the defect density and eliminates the need for any post-growth treatments. This approach enables precise control over defect density while ensuring large-area uniformity and fabrication scalability. The resulting OES devices exhibit excellent photoresponsivity (10.3 A W<sup>-1</sup>) and stable synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, short-term memory, long-term memory, and spike-timing-dependent plasticity. Remarkably, the device mimics human learning and forgetting with an ultralow energy consumption of 1.2 fJ per synaptic event, outperforming the energy efficiency of biological synapses (∼10 fJ). Furthermore, an artificial neural network trained using device-derived parameters achieves a recognition accuracy of 87.1% on the MNIST handwritten digit dataset. Density functional theory calculations elucidate the crucial role of <i>in situ</i> engineered sulfur vacancies in modulating carrier dynamics and defect-assisted charge trapping, providing a fundamental understanding of the light-induced synaptic behavior in the device.

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Journal
Nanoscale
Published
2025-11-24
DOI
https://doi.org/10.1039/d5nr03748d
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Superior performance of printed optoelectronic synapses based on defect-controlled monolayer MoS 2 with ultralow power consumption for neuromorphic computing

P. K. Giri, Meyya Meyyappan, Subhankar Debnath, Abdul Kaium Mia
Nanoscale
Advanced Memory and Neural Computing
article

Superior performance of printed optoelectronic synapses based on defect-controlled monolayer MoS 2 with ultralow power consumption for neuromorphic computing

P. K. Giri, Meyya Meyyappan, Subhankar Debnath, Abdul Kaium Mia
article en

Abstract

Optoelectronic synapses (OES), which integrate photodetection and synaptic functions in a single platform, offer a promising approach to mimic the visual processing capabilities of the human brain. Two-dimensional (2D) materials are attractive for OES devices due to their excellent energy efficiency and high photoelectric conversion capability. However, most 2D material-based OES devices rely on conventional lithography and post-growth defect engineering, both of which require expensive cleanroom facilities and complex processing steps. Here, we present a hybrid fabrication strategy that integrates cost-effective printing technology, as an alternative to conventional lithography, with <i>in situ</i> defect engineering during CVD growth of monolayer MoS<sub>2</sub>. The latter intrinsically tunes the defect density and eliminates the need for any post-growth treatments. This approach enables precise control over defect density while ensuring large-area uniformity and fabrication scalability. The resulting OES devices exhibit excellent photoresponsivity (10.3 A W<sup>-1</sup>) and stable synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, short-term memory, long-term memory, and spike-timing-dependent plasticity. Remarkably, the device mimics human learning and forgetting with an ultralow energy consumption of 1.2 fJ per synaptic event, outperforming the energy efficiency of biological synapses (∼10 fJ). Furthermore, an artificial neural network trained using device-derived parameters achieves a recognition accuracy of 87.1% on the MNIST handwritten digit dataset. Density functional theory calculations elucidate the crucial role of <i>in situ</i> engineered sulfur vacancies in modulating carrier dynamics and defect-assisted charge trapping, providing a fundamental understanding of the light-induced synaptic behavior in the device.

NanoscaleVol. 18(1)
Indian Institute of Technology Guwahati (IN)
Openalex Percentile: Top 64%
Advanced Memory and Neural Computing
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