Reservoir computing (RC) system based on photoelectronic memristors has attracted increasing attention because of its inherently nonlinearity, history-dependent dynamics, and fading memory for real-time processing complex task. However, relying on its natural relaxation process leads to the RC system with low efficiency. Here, we propose an Au/CuI/TiOx/FTO heterojunction optoelectronic synergistic memristor and demonstrate its application as a single-node physical reservoir computing (PRC) system. The device synergistically integrates oxygen-vacancy migration in TiOx with the photosensitivity of CuI, enabling precise. This approach generates rich, high-dimensional reservoir states with enhanced separability. The memristor exhibits diverse synaptic plasticity and supports over 32 distinct conductance states (>5-bits precision). Furthermore, a predictive model was established to capture device dynamics, achieving an accuracy of 82.8% for the standard RC model, while the accuracy of the heterojunction memristor-assisted RC model increased to 97.26%. The research demonstrates that heterojunction optoelectronic collaborative memristors exhibit significant potential in efficient temporal encoding and high-precision single-node PRC, providing a promising solution for edge intelligence and low-power neuromorphic hardware.
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Lei Fan
Marquette University
Jinze Li
University of Science and Technology Liaoning
Xin Su
Southwest University
ACS Applied Electronic Materials
Xi'an Jiaotong University
Southwest University
Shanghai Center for Brain Science and Brain-Inspired Technology
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Fan et al. (Sun,) studied this question.
synapsesocial.com/papers/69df2c1de4eeef8a2a6b1120 — DOI: https://doi.org/10.1021/acsaelm.6c00484
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