Bioinspired Bimodal Artificial Afferent Nerve Based on AgBiS 2 QD/IGZO Heterojunction Optoelectronic Synaptic Transistor for Visuo‐Tactile Fusion Perception

ABSTRACT Reliable perception in complex and dynamically degraded environments remains a central challenge for both biological nervous systems and next‐generation embodied intelligent systems. Biological organisms mitigate uncertainty via complementary bimodal perception, where various sensory modalities jointly sustain adaptive behaviors. Inspired by the star‐nosed mole, we report an artificial visuo‐tactile afferent nerve based on an AgBiS 2 quantum dot (QD)/IGZO heterojunction optoelectronic synaptic transistor. The artificial nerve supports sensory acquisition, neural‐like signal encoding, synaptic modulation, and adaptive actuation within a unified neuromorphic framework. Benefiting from interfacial carrier separation and persistent photoinduced charge dynamics, this synaptic transistor exhibits reconfigurable synaptic plasticity with intrinsically coupled bimodal synaptic modulation. External visuo‐tactile stimuli are first transformed into unified neuromorphic responses and subsequently mapped to threshold‐governed robotic behaviors, including directional motion, turning, and grasping. Device‐informed results demonstrate that device‐enabled bimodal cooperation enhances recognition robustness under degraded perceptual conditions. This work establishes a compact, physically grounded framework integrating bimodal perception and adaptive intelligence, with potential implications for next‐generation bioinspired neuromorphic systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-08-21
DOI
https://doi.org/10.1002/adfm.77842
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Bioinspired Bimodal Artificial Afferent Nerve Based on AgBiS 2 QD/IGZO Heterojunction Optoelectronic Synaptic Transistor for Visuo‐Tactile Fusion Perception

Qixuan Zhong, Ruchao Gong, Jialin Yao, Huafei Guo et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Bioinspired Bimodal Artificial Afferent Nerve Based on AgBiS 2 QD/IGZO Heterojunction Optoelectronic Synaptic Transistor for Visuo‐Tactile Fusion Perception

Qixuan Zhong, Ruchao Gong, Jialin Yao, Huafei Guo, Sai Jiang, Jinrong Huang, Sheng Li, Zihan Wang, Chengyao Yang, Lanyu Nie, Yong Dai, Zhicheng Wang
article en

Abstract

ABSTRACT Reliable perception in complex and dynamically degraded environments remains a central challenge for both biological nervous systems and next‐generation embodied intelligent systems. Biological organisms mitigate uncertainty via complementary bimodal perception, where various sensory modalities jointly sustain adaptive behaviors. Inspired by the star‐nosed mole, we report an artificial visuo‐tactile afferent nerve based on an AgBiS 2 quantum dot (QD)/IGZO heterojunction optoelectronic synaptic transistor. The artificial nerve supports sensory acquisition, neural‐like signal encoding, synaptic modulation, and adaptive actuation within a unified neuromorphic framework. Benefiting from interfacial carrier separation and persistent photoinduced charge dynamics, this synaptic transistor exhibits reconfigurable synaptic plasticity with intrinsically coupled bimodal synaptic modulation. External visuo‐tactile stimuli are first transformed into unified neuromorphic responses and subsequently mapped to threshold‐governed robotic behaviors, including directional motion, turning, and grasping. Device‐informed results demonstrate that device‐enabled bimodal cooperation enhances recognition robustness under degraded perceptual conditions. This work establishes a compact, physically grounded framework integrating bimodal perception and adaptive intelligence, with potential implications for next‐generation bioinspired neuromorphic systems.

Advanced Functional Materials
Hangzhou Normal University (CN), Collaborative Innovation Center of Advanced Microstructures (CN), Energy International (United States) (US), Changzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province
Partnerships for the goals
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
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