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Updated: Mar 18, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Nonvolatile Optoelectronic Synapses and Brain-Inspired Neuromorphic Applications Based on NbOCl2/α-In2Se3
Weidong Dai1, Cheng Qi1, Jinpeng Zhao1
1College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang 421008, China.
Abstract:
Two-dimensional van der Waals heterostructures have emerged as promising candidates for next-generation optoelectronic devices owing to their tunable band structures and strong light-matter interactions. However, achieving high-performance photodetection and neuromorphic functionalities within a single platform remains a major challenge, particularly in controlling distinct charge transport pathways of the same material system. Here, we report a multifunctional NbOCl2/α-In2Se3 heterojunction device that integrates three conductive pathways: a NbOCl2 channel, a hybrid vertical transport channel combining NbOCl2 and α-In2Se3, and an α-In2Se3 surface channel. This unique trichannel design enables efficient parallel carrier transport, resulting in remarkable optoelectronic performance characterized by a high responsivity of 2933 A/W, an ultrahigh external quantum efficiency of 7.67 × 105%, and a detectivity of 2.29 × 1012 Jones. Furthermore, the device exhibited fast photoresponse characteristics, with fast response time of 52 ms and slow decay time of 2122 ms. More importantly, the device demonstrates superior synaptic plasticity with high learning retention of 6.58 μA and low forgetting rate of 0.073, as quantitatively described by the Wickelgren power-law model. Using the optoelectronic synaptic behavior of the device, we simulated learning and forgetting in neural networks, and achieved an accuracy of 92.5% on the MNIST handwritten digit data set. This work highlights the potential of NbOCl2/α-In2Se3 heterostructures as a versatile platform for integrated optoelectronic systems, enabling intelligent sensing and neuromorphic computing.
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