An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse

Artificial synapses have emerged as a pivotal technological advancement in mimicking brain functions. Organic memristors are desirable for hardware implementation of artificial synapses, owing to their remarkable mechanical flexibility, high biocompatibility at cell-device interfaces, and adjustable material structure. Developing appropriate organic polymers with carbon dots modification will enable the memristor to possess analog-type resistive switching behavior, crucial for realizing brain-like associative learning and adapting dynamic variations of neuron connection strength. In this work, an artificial synapse based on the analogue organic memristor integrating neuromorphic computing and neural interface functions is proposed, utilizing synthetic conjugated porous polymers to construct composites with boron-doped carbon dots. The structure-property relationship of alkynyl and alkyl chains in polymers is elucidated, alongside the synergistic effect of local photoinduced redox and hole templating in composites that endows the device with analog-type resistive switching behavior. Moreover, the memristor presents impressive synaptic plasticity and associative memory learning potential for neuromorphic computing, and further serves as a core unit in flexible artificial neural interface chips, demonstrating dynamic information transmission with neural systems. This study will promote the further development of organic artificial synapses for neuromorphic computing and brain-machine interfaces.

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Publication Details

Journal
Exploration
Published
2026-02-01
DOI
https://doi.org/10.1002/exp.20250234
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse

Mixue Wang, Haotian Hao, Jiyun Zhang, Meiwen An et al.
Exploration
Advanced Memory and Neural Computing
article

An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse

Mixue Wang, Haotian Hao, Jiyun Zhang, Meiwen An, Xiaochen Lang, Linlin Shi, Jintao He, Yizhi Zhu, Xiong Jiao, Lin Chen, Yongzhen Yang, Guangdong Zhou
article en

Abstract

Artificial synapses have emerged as a pivotal technological advancement in mimicking brain functions. Organic memristors are desirable for hardware implementation of artificial synapses, owing to their remarkable mechanical flexibility, high biocompatibility at cell-device interfaces, and adjustable material structure. Developing appropriate organic polymers with carbon dots modification will enable the memristor to possess analog-type resistive switching behavior, crucial for realizing brain-like associative learning and adapting dynamic variations of neuron connection strength. In this work, an artificial synapse based on the analogue organic memristor integrating neuromorphic computing and neural interface functions is proposed, utilizing synthetic conjugated porous polymers to construct composites with boron-doped carbon dots. The structure-property relationship of alkynyl and alkyl chains in polymers is elucidated, alongside the synergistic effect of local photoinduced redox and hole templating in composites that endows the device with analog-type resistive switching behavior. Moreover, the memristor presents impressive synaptic plasticity and associative memory learning potential for neuromorphic computing, and further serves as a core unit in flexible artificial neural interface chips, demonstrating dynamic information transmission with neural systems. This study will promote the further development of organic artificial synapses for neuromorphic computing and brain-machine interfaces.

ExplorationVol. 6(1)
Southwest University (CN), Shanxi Medical University (CN), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Taiyuan University of Science and Technology (CN), Taiyuan University of Technology (CN)
National Natural Science Foundation of China, Shanxi Scholarship Council of China
Openalex Percentile: Top 59%
Advanced Memory and Neural Computing
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