Tri‐Phase in‐Material Reconfigurable Neuromorphic Devices for Extreme Environmental Adaptive Perception

ABSTRACT Achieving multimodal reconfiguration within a single neuromorphic device is pivotal for low‐power, highly parallel brain‐inspired computing. Conventional reconfigurable memristors rely on conductive filaments, which are stochastic and thermally unstable, limiting device reliability. Here, we report an intrinsically reconfigurable neuromorphic device based on a thermally driven tri‐phase‐interconverted supramolecular liquid crystal (SLC). The device reversibly switches among three distinct molecular phases‐superlattice (SL), lamellar quadruple (LQ), and isotropy (ISO). The SL phase enables linearly programmable conductance modulation, while the LQ phase exhibits bistability within a narrow switching window, achieving excellent cycle‐to‐cycle (>99%) and device‐to‐device (>95%) uniformity, along with stable endurance over 105 cycles at 435 K. The device implements self‐recovery through thermally‐induced molecular re‐organization (>95% consistency over 104 cycles), manifesting robust intrinsic reconfigurability. Furthermore, an environmentally adaptive neuromorphic system is constructed for complex perception and classification tasks in wide‐temperature‐range extreme environments. This three‐states‐in‐one‐device paradigm offers a transformative platform for highly‐efficient, multifunctional, and temperature‐adaptive neuromorphic hardware integrating memory, computation, and encryption.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-05-22
DOI
https://doi.org/10.1002/adfm.76002
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Tri‐Phase in‐Material Reconfigurable Neuromorphic Devices for Extreme Environmental Adaptive Perception

Huanjun Lu, Y T Li, C Zhang, Wan Qian et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Tri‐Phase in‐Material Reconfigurable Neuromorphic Devices for Extreme Environmental Adaptive Perception

Huanjun Lu, Y T Li, C Zhang, Wan Qian, Qi Wang, Huipeng Chen, Lixuan Liu, Xicheng Guo, Chun Zhao, Zheng Xu, Shijie Chen, Fangchao Li, Chunlan Ma
article en

Abstract

ABSTRACT Achieving multimodal reconfiguration within a single neuromorphic device is pivotal for low‐power, highly parallel brain‐inspired computing. Conventional reconfigurable memristors rely on conductive filaments, which are stochastic and thermally unstable, limiting device reliability. Here, we report an intrinsically reconfigurable neuromorphic device based on a thermally driven tri‐phase‐interconverted supramolecular liquid crystal (SLC). The device reversibly switches among three distinct molecular phases‐superlattice (SL), lamellar quadruple (LQ), and isotropy (ISO). The SL phase enables linearly programmable conductance modulation, while the LQ phase exhibits bistability within a narrow switching window, achieving excellent cycle‐to‐cycle (>99%) and device‐to‐device (>95%) uniformity, along with stable endurance over 105 cycles at 435 K. The device implements self‐recovery through thermally‐induced molecular re‐organization (>95% consistency over 104 cycles), manifesting robust intrinsic reconfigurability. Furthermore, an environmentally adaptive neuromorphic system is constructed for complex perception and classification tasks in wide‐temperature‐range extreme environments. This three‐states‐in‐one‐device paradigm offers a transformative platform for highly‐efficient, multifunctional, and temperature‐adaptive neuromorphic hardware integrating memory, computation, and encryption.

Advanced Functional Materials
Suzhou University of Science and Technology (CN), Soochow University (CN), Suzhou Institute of Nano-tech and Nano-bionics (CN), Xi’an Jiaotong-Liverpool University (CN), Fuzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province
Life in Land
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
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