2D transition metal dichalcogenides for energy-efficient two-terminal optoelectronic synaptic devices

<h2>Summary</h2> Two-dimensional layered transition metal dichalcogenides (2D TMDCs), such as tungsten disulfide, molybdenum disulfide, compounds based on rhenium, and their heterostructures, have been used to fabricate artificial synaptic devices that combine memory, computation, and sensing in a single system. By using a combination of optoelectronic/electronic signal processing systems, these devices have demonstrated multi-state memory, pattern-recognition capabilities, biological synaptic behavior, and visual information processing. Their advanced scalability and integration potential render them ideal candidates for emerging neuromorphic memories in edge AI and wearable devices. Although ultra-low power consumption in neuromorphic vision systems in the range of femtojoules has been achieved, optimizing the materials' quality and controlling the defect formation are still required to enhance their functionality and improve the devices' performance. Improving the scalability of heterostructures and integrating many single devices in arrays operating as part of a neuromorphic system are paramount to their commercialization.

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Publication Details

Journal
Device
Published
2025-05-23
DOI
https://doi.org/10.1016/j.device.2025.100805
Citations
9
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
6.57

Funders

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article

2D transition metal dichalcogenides for energy-efficient two-terminal optoelectronic synaptic devices

Roshni Satheesh Babu, Dimitra G. Georgiadou
9 citations
Device
Advanced Memory and Neural Computing
6.57
article

2D transition metal dichalcogenides for energy-efficient two-terminal optoelectronic synaptic devices

Roshni Satheesh Babu, Dimitra G. Georgiadou
article en
9 citations

Abstract

<h2>Summary</h2> Two-dimensional layered transition metal dichalcogenides (2D TMDCs), such as tungsten disulfide, molybdenum disulfide, compounds based on rhenium, and their heterostructures, have been used to fabricate artificial synaptic devices that combine memory, computation, and sensing in a single system. By using a combination of optoelectronic/electronic signal processing systems, these devices have demonstrated multi-state memory, pattern-recognition capabilities, biological synaptic behavior, and visual information processing. Their advanced scalability and integration potential render them ideal candidates for emerging neuromorphic memories in edge AI and wearable devices. Although ultra-low power consumption in neuromorphic vision systems in the range of femtojoules has been achieved, optimizing the materials' quality and controlling the defect formation are still required to enhance their functionality and improve the devices' performance. Improving the scalability of heterostructures and integrating many single devices in arrays operating as part of a neuromorphic system are paramount to their commercialization.

DeviceVol. 3(9)
University of Southampton (GB)
UK Research and Innovation, European Commission, HORIZON EUROPE Framework Programme, HORIZON EUROPE Widening Participation and Strengthening the European Research Area
Affordable and clean energy
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
6.57
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