Issue

Vol. 18 (2026)

Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing

https://doi.org/10.1007/s40820-026-02171-2
Dunan Hu
State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310058, People’s Republic of China
Ruqi Yang
State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310058, People’s Republic of China
Zhizhen Ye
State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310058, People’s Republic of China
Jianguo Lu
State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310058, People’s Republic of China

Corresponding Author: Jianguo Lu

lujianguo@zju.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 320

Abstract

The rapid expansion of artificial intelligence has led to significant challenges in energy consumption and computational efficiency. To address these issues, the exploration and development of all-optical controlled (AOC) synaptic devices represents a promising leap forward in neuromorphic computing, offering potential solutions to the inherent limitations of traditional von Neumann architectures. AOC synaptic devices, utilizing exclusively optical signals to emulate bidirectional modulation of synaptic weights, bypass the complexity and additional energy costs associated with conventional electrical or electro-optical hybrid signals. This review articulates the underlying framework and fundamental motivations for studying AOC synapses, while systematically reviewing current research progress. We particularly highlight the synergistic relationships among physical mechanisms, material behaviors, and device architectures, as well as neuromorphic computing based on optical writing and optical erasing of information. By systematically interpreting these multidimensional correlations, we propose scalable and reproducible strategies for device design. This work will certainly herald a substantial direction of AOC synapses, providing an ideal platform for exploring neuromorphic computing for artificial intelligence.


Highlights:
1 Neuromorphic computing, using artificial synaptic devices, mimics the structure and function of biological neural networks, offering a solution to the von Neumann bottleneck for the next-generation of artificial intelligence.
2 All-optical controlled synapses enable bidirectional modulation of conductivity through light pulses for both optical writing and optical erasing of information, crucial for hardware breakthroughs in neuromorphic computing.
3 A scalable and replicable design framework for all-optical controlled synaptic devices could play a pivotal role in advancing neuromorphic computing for a new era of artificial intelligence.

Keywords

All-optical control Artificial synapse Device mechanisms Design framework Neuromorphic computing
Hu, Dunan, Ruqi Yang, Zhizhen Ye, and Jianguo Lu. 2026. “Underlying Framework of All-Optical Controlled Synaptic Devices for Neuromorphic Computing”. Nano-Micro Letters 18 (April):320. https://doi.org/10.1007/s40820-026-02171-2.
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