Optoelectronic Synergy Reservoir Computing System Based on CuI-TiO <sub> <i>x</i> </sub> Heterojunction Memristor for High Precision Image Processing
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.
Authors
- Lei Fan (ORCID: https://orcid.org/0000-0001-9472-7152)
- Guangdong Zhou (ORCID: https://orcid.org/0000-0002-5824-9488)
- Jinze Li (ORCID: https://orcid.org/0000-0003-0697-057X)
- Xiude Yang (ORCID: https://orcid.org/0000-0002-8921-2776)
- Bo Wu
- Xiuxia Wang
- Siqi Luo
- Ping Li
- Xin Su
Institutions
- Southwest University (CN)
- Zunyi Normal College (CN)
- Frontier Science Foundation (CH)
- Department of Physics, Mathematics and Informatics (BY)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- ACS Applied Electronic Materials
- Published
- 2026-04-13
- DOI
- https://doi.org/10.1021/acsaelm.6c00484
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chongqing Graduate Student Research Innovation Project
- Fundamental Research Funds for Central Universities of the Central South University