Intrinsically Nonvolatile n‐Type Polymer Synapses for Neuromorphic Vision

Neuromorphic vision requires photonic synapses capable of integrating optical sensing and memory. However, achieving synaptic plasticity in photonic synapses typically relies on auxiliary charge-trapping components, resulting in increased processing complexity and poor substrate compatibility that limit large-area integration. Although single-component photonic synapses represent an attractive alternative, their development has been hindered by the lack of rational material-design strategies. Here, we introduce an asymmetric carrier trapping (ACT) strategy that enables intrinsic nonvolatile photoresponse in n-type conjugated polymers. Through tailored frontier-orbital energies and distributions, ACT combines a deep-lying, delocalized lowest unoccupied molecular orbital (LUMO) for efficient electron transport with a localized highest occupied molecular orbital (HOMO) for stable hole trapping. Guided by computational screening, we fabricated single-polymer photonic synapses across diverse substrates. Enabled by intrinsic ACT behavior, these synapses operate without elaborate interfacial engineering while simultaneously performing optical signal reception, storage, and brain-like synaptic functions. Furthermore, an 8 × 8 flexible synaptic array exhibited highly uniform performance with paired-pulse facilitation (PPF) indices of 137 ± 1%. An artificial neural network (ANN) constructed using these polymer synapses achieved an image-recognition accuracy of 93.3% under ambient conditions. These findings establish ACT as a promising molecular strategy for intrinsically nonvolatile polymer synapses toward neuromorphic vision.

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Publication Details

Journal
Advanced Materials
Published
2026-09-04
DOI
https://doi.org/10.1002/adma.74890
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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Intrinsically Nonvolatile n‐Type Polymer Synapses for Neuromorphic Vision

Jie‐Yu Wang, Hua‐Kang Kong, Tianyu Zhang, Zi‐Di Yu et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Intrinsically Nonvolatile n‐Type Polymer Synapses for Neuromorphic Vision

Jie‐Yu Wang, Hua‐Kang Kong, Tianyu Zhang, Zi‐Di Yu, Ze‐Fan Yao, Jian Pei, Xiao-Yan Zhang, Yao‐Xi Long, Yuan‐Kai Li
article en

Abstract

Neuromorphic vision requires photonic synapses capable of integrating optical sensing and memory. However, achieving synaptic plasticity in photonic synapses typically relies on auxiliary charge-trapping components, resulting in increased processing complexity and poor substrate compatibility that limit large-area integration. Although single-component photonic synapses represent an attractive alternative, their development has been hindered by the lack of rational material-design strategies. Here, we introduce an asymmetric carrier trapping (ACT) strategy that enables intrinsic nonvolatile photoresponse in n-type conjugated polymers. Through tailored frontier-orbital energies and distributions, ACT combines a deep-lying, delocalized lowest unoccupied molecular orbital (LUMO) for efficient electron transport with a localized highest occupied molecular orbital (HOMO) for stable hole trapping. Guided by computational screening, we fabricated single-polymer photonic synapses across diverse substrates. Enabled by intrinsic ACT behavior, these synapses operate without elaborate interfacial engineering while simultaneously performing optical signal reception, storage, and brain-like synaptic functions. Furthermore, an 8 × 8 flexible synaptic array exhibited highly uniform performance with paired-pulse facilitation (PPF) indices of 137 ± 1%. An artificial neural network (ANN) constructed using these polymer synapses achieved an image-recognition accuracy of 93.3% under ambient conditions. These findings establish ACT as a promising molecular strategy for intrinsically nonvolatile polymer synapses toward neuromorphic vision.

Advanced Materials
Beijing National Laboratory for Molecular Sciences (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
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