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Research Article | Open Access | Just Accepted

Low-power reconfigurable MoS2/MoTe2 optoelectronic synapse for visual recognition

Xin Yan1,2,3Wen Deng2,3Niannian Yu2Jinsong Wu3Xiuying Zhang2Wen Luo1,2,3( )

1 Hubei Longzhong Laboratory, Wuhan University of Technology (Xiangyang Demonstration Zone) Xiangyang, Hubei 441000, China

2 Department of Physics, School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China

3 State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China

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Abstract

Doping with impurity defects or sustained multi-terminal external electric fields can enhance performance of artificial optoelectronic synapses based on two-dimensional materials. But doping causes varying degrees of damage to the original lattice structure, while external fields would increase additional power consumption. Here, we demonstrate an effective surface charge transfer doping approach sensitive to air that facilitates the fabrication of reconfigurable MoS2/MoTe2 devices. MoS2/MoTe2 undergoes electron transfer with surface-adsorbed O2/H2O, resulting in varying degrees of p-type doping that affects the Schottky barrier and the built-in electric field strength at the PN junction. The doping level can be reconstructed by a brief gate bias resulting in controllable photocurrent. Due to the conduction of the reverse PN junction, the low dark current and high photoelectric response result in an extremely low power consumption per detectable spike (0.73 pJ), and stability is maintained during an 80,000 s reconstruction process. Notably, hardware-level self-noise reduction is achieved through feature-based long/short-term memory, and recognition accuracy on the processed MNIST dataset improved 39%. The unique photo-electro co-modulation strategy paves a promising path for future development of artificial vision systems.

Nano Research
Cite this article:
Yan X, Deng W, Yu N, et al. Low-power reconfigurable MoS2/MoTe2 optoelectronic synapse for visual recognition. Nano Research, 2025, https://doi.org/10.26599/NR.2025.94907741

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Received: 21 March 2025
Revised: 05 June 2025
Accepted: 27 June 2025
Available online: 27 June 2025

© The Author(s) 2025. Published by Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/)

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