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Vol. 19 (2027)

Spectrally Partitioned All-Optical Memristors for Highly Linear Neuromorphic Vision Systems

https://doi.org/10.1007/s40820-026-02331-4
Junchao Zhang
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Bai Sun
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Songling Wang
State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, People’s Republic of China
Guangdong Zhou
College of Artificial Intelligence, Brain‑Inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, People’s Republic of China
Linfeng Sun
Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Zelin Cao
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Kaikai Gao
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Xiaojun Li
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Xiaoliang Chen
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China
Jinyou Shao
Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China

Corresponding Author: Jinyou Shao

jyshao@xjtu.edu.cn

Nano-Micro Letters, Vol. 19 (2027), Article Number: 42

Abstract

All-optical control of synaptic weights offers a promising route toward low-power and massively parallel neuromorphic hardware. However, existing optoelectronic memristors often require electrical assistance or exhibit nonlinear and asymmetric conductance updates, limiting their energy efficiency and scalability. Herein, we report a spectrally partitioned all-optical memristor based on a PbS/PEDOT:PSS/VOx heterostructure, in which near-infrared and visible light are coupled to opposite defect-state filling and depletion pathways. Near-infrared illumination selectively excites PbS quantum dots and drives gradual electron transfer into oxygen-vacancy-related defect states in VOx through the PEDOT:PSS-regulated interface, producing continuous conductance potentiation. By contrast, visible illumination activates VOx and promotes interfacial hole-assisted recombination, leading to reversible depletion of defect-state electrons and conductance depression. This wavelength-selective carrier-transfer process enables highly linear and symmetric all-optical weight updates, achieving a linearity of 0.9994 and an effective 8-bit conductance resolution. The electrical energy consumption per optically induced synaptic event is calculated to be as low as 0.63 fJ under an ultralow probing bias. By integrating the devices into a 32 × 32 all-optically programmed array, we further demonstrate hardware-based BloodMNIST microscopic blood-cell image classification with an accuracy of 96.5% and strong noise robustness. These results provide a viable strategy for developing high-linearity, low-power, and spectrally programmable neuromorphic hardware.


Highlights:
1 A spectrally partitioned PbS/PEDOT:PSS/VOx all-optical memristor enables wavelength-selective potentiation and depression through regulated interfacial carrier dynamics.
2 Highly linear bidirectional weight updates with a linearity of 0.9994, 8-bit conductance resolution, and an electrical energy consumption of 0.63 fJ per event are achieved.
3 A 32 × 32 memristor array enables hardware-based BloodMNIST inference with 96.5% accuracy and strong noise robustness.

Keywords

All-optical memristor Neuromorphic vision Brain-like chip Hardware neural network Artificial intelligence
Zhang, Junchao, Bai Sun, Songling Wang, Guangdong Zhou, Linfeng Sun, Zelin Cao, Kaikai Gao, Xiaojun Li, Xiaoliang Chen, and Jinyou Shao. 2026. “Spectrally Partitioned All-Optical Memristors for Highly Linear Neuromorphic Vision Systems”. Nano-Micro Letters 19 (August):42. https://doi.org/10.1007/s40820-026-02331-4.
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