@article{Li2026, 
author = {Pengfei Li and Weifeng Zhang and Biao Yang and Guojun Duan and Jiameng Sun and Xiaobing Yan},
title = {A multifunctional optoelectronic memristor based on Sb2Se3 nanorod arrays for high-dimensional reservoir computing},
year = {2026},
journal = {Nano Research},
keywords = {optoelectronic memristors, Sb2Se3 nanorod arrays, in-sensor computing, synaptic plasticity, high-dimensional reservoir computing},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94909054},
doi = {10.26599/NR.2026.94909054},
abstract = {The emerging paradigm of in-sensor computing demands the seamless integration of optical sensing, memory, and localized data processing within a single monolithic architecture. However, existing integrated devices face a fundamental physical trade-off between rapid photogenerated carrier extraction for sensing and prolonged carrier retention for synaptic memory, severely limiting their overall processing efficiency. To break this bottleneck, a multifunctional optoelectronic memristor based on 1D Pd/Sb2Se3/TiN nanorod arrays is demonstrated. The unique 1D nanorod architecture helps to mitigate grain boundary recombination, granting the device exceptional multimodal capabilities. Operating as a pure photodetector, it exhibits an ultrafast response time of 35 μs and a high ON/OFF ratio of ~ 2×104. Under optoelectronic co-modulation, the intrinsic carrier decay dynamics precisely emulate robust synaptic plasticity with an ultralow energy consumption of ~3.6 pJ per spike. Leveraging this reliable nonlinear responsiveness to mixed optoelectronic stimuli, a dual-feature extraction strategy is implemented to construct a high-dimensional reservoir computing (RC) system. This approach efficiently enhances computational dimensionality without redundant network complexity, achieving outstanding recognition accuracies of 90.4% on the MNIST dataset and 92.3% in dynamic hand posture recognition tasks. This work provides a fundamental material-to-system paradigm for developing energy-efficient artificial visual systems and advanced in-sensor computing architectures.}
}