Bio‐Inspired Sb <sub>2</sub> S <sub>3</sub> Memristors Supporting Dual‐Mode Operation via Self‐Regulating Transport Pathways for All‐in‐one Neuromorphic Perception and Computation

Abstract The brain‐inspired dual‐mode memristor integrates analog conductance modulation and digital threshold switching, enabling dynamic adaptability and real‐time responses in a single‐device architecture for further all‐in‐one neuromorphic computing. Antimony sulfide (Sb 2 S 3 ) exhibits unique electronic and optical properties, making it ideal for above smart memory systems. However, current studies achieve only single synaptic functionality with monotonous conductive mechanisms and lack dual‐mode collaborative regulation strategies for complex sensing‐computing integration. Herein, a polymethyl methacrylate (PMMA)‐engineered Ag/Sb 2 S 3 /FTO memristor (APSF) is developed, which enables voltage‐gated analog‐to‐digital hybrid switching to bridge this gap. The introduced PMMA layer acts as an interface barrier for Ag⁺ migration and a mediator for sulfur vacancy (V S ) pathways, enabling dual conduction via Ag filaments and V S pathways. The voltage‐regulated digital‐to‐analog mode transition in APSF devices originates from bias‐coordinated charge trapping/release by the polar functional groups and interchain voids of PMMA, allowing single‐device integration of artificial synaptic and nociceptive functions. Moreover, the memristor array achieves 93.09% recognition accuracy and enables real‐time edge detection and motion tracking through in‐memory computing for convolutional processing. The results introduce an innovative paradigm for exploring multi‐functional memristor materials and devices.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-08-03
DOI
https://doi.org/10.1002/adfm.202510212
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46

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article

Bio‐Inspired Sb 2 S 3 Memristors Supporting Dual‐Mode Operation via Self‐Regulating Transport Pathways for All‐in‐one Neuromorphic Perception and Computation

Hongling Guo, Fang Wang, Kailiang Zhang, Kai Hu et al.
2 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
1.46
article

Bio‐Inspired Sb 2 S 3 Memristors Supporting Dual‐Mode Operation via Self‐Regulating Transport Pathways for All‐in‐one Neuromorphic Perception and Computation

Hongling Guo, Fang Wang, Kailiang Zhang, Kai Hu, Yuchan Wang, Xiaolei Li, Lin Feng, Yuanhui Yang, Lei Zheng, Shifu Xiong, Yuanjie Yang
article en
2 citations

Abstract

Abstract The brain‐inspired dual‐mode memristor integrates analog conductance modulation and digital threshold switching, enabling dynamic adaptability and real‐time responses in a single‐device architecture for further all‐in‐one neuromorphic computing. Antimony sulfide (Sb 2 S 3 ) exhibits unique electronic and optical properties, making it ideal for above smart memory systems. However, current studies achieve only single synaptic functionality with monotonous conductive mechanisms and lack dual‐mode collaborative regulation strategies for complex sensing‐computing integration. Herein, a polymethyl methacrylate (PMMA)‐engineered Ag/Sb 2 S 3 /FTO memristor (APSF) is developed, which enables voltage‐gated analog‐to‐digital hybrid switching to bridge this gap. The introduced PMMA layer acts as an interface barrier for Ag⁺ migration and a mediator for sulfur vacancy (V S ) pathways, enabling dual conduction via Ag filaments and V S pathways. The voltage‐regulated digital‐to‐analog mode transition in APSF devices originates from bias‐coordinated charge trapping/release by the polar functional groups and interchain voids of PMMA, allowing single‐device integration of artificial synaptic and nociceptive functions. Moreover, the memristor array achieves 93.09% recognition accuracy and enables real‐time edge detection and motion tracking through in‐memory computing for convolutional processing. The results introduce an innovative paradigm for exploring multi‐functional memristor materials and devices.

Advanced Functional MaterialsVol. 36(5)
Tianjin University of Science and Technology (CN), Tianjin University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 16%
Advanced Memory and Neural Computing
1.46
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