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Organic synapses with programmable linearity for neuromorphic computing

delete2026-07-31
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PRE
AI
Y
Yincheng Zhang
W
Wenwan Zeng
H
Hao Chen *
Y
Yunjie Tian
Q
Qijie Lin
Y
Yifan Liu
C
Cong Shan
邱丽 cover
邱丽 (Li Qiu)
S
Siyuan Liu
Y
Yunhao Cai
彭谦 (Qian Peng) *
H
Hui Huang *
DOI:10.1038/s41563-026-02689-1delete
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Abstract

Abstract

En 中文
Organic synaptic devices offer a route to flexible and biocompatible neuromorphic computing and human–machine interfaces. However, electrical signal transmission is often nonlinear and poorly reproducible because of interfacial effects and non-uniform electronic processes that can increase energy consumption. Organic all-photonic synapses circumvent electrical transmission but remain limited by nonlinear photochemical and photoisomerization processes. Here we develop linearity-programmable organic all-photonic synapses based on a charge-separated-buffered adaptive luminescence mechanism. Systematic engineering of guest molecular structures modulates charge-separation kinetics, allowing precise control over synaptic linearity. The resulting devices exhibit a linearity parameter, v, of 0.0093, 99% uniformity, 97% repeatability, an optical trigger energy of 59 zJ per synaptic event and a response time of 1.39 ns. An all-photonic sensor system integrating linearity-programmable organic all-photonic synapses enables high-quality image acquisition and high image-classification accuracy. These results establish a molecularly programmable photophysical platform for neuromorphic signal processing and provide a potential route towards low-energy human–machine interfaces. Interfacial effects and nonlinear electronic processes hinder the development of high-performance organic artificial synapses. A highly efficient organic all-photonic synapse is developed with linear synaptic responses achieved by optically tailored charge-separation kinetics.

Journal

Nature Materials cover
Nature Materials
IF:
38.5
Papers:
6.7K
Citations:
11.5W

Organization

T
tianjin university
Scholars:
7.7W
Papers: 5.6W
Citations: 88
U
University of Chinese Academy of Sciences
Scholars:
5.7K
Papers: 2.3K
Citations: 24.6W
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