Intrinsically flexible multimode reconfigurable transistors for polymorphic circuits and neuromorphic devices

Reconfigurable transistors hold considerable significance for both integrated circuits and neuromorphic electronics. At the same time, flexible electronics is developing rapidly, promoted by various emerging applications, such as wearable electronics and smart robots. Therefore, flexible reconfigurable transistors are highly expected to create emerging application scenarios while they are seldom reported. Here, intrinsically flexible multimode reconfigurable transistors (IFMRTs) with dual-gate structure are proposed and realized. Either top or bottom gate can serve as the modulation terminal to switch the transistor among p-type, n-type, and ambipolar modes. The threshold voltage of the reconfigurable transistor is modulable. Based on IFMRTs, we have demonstrated inverters and polymorphic logic circuits with key bit-selectable NAND or NOR logic functions, which provides solutions for hardware security. We have also demonstrated artificial heterosynapse and dendrite, achieving reconfigurable synaptic responses and dendrite integration. Taking the simulation of automatic obstacle avoidance and coordination control of hand movement by low-level and high-level neural centers as examples, we illustrate the potential intelligence of IFMRTs in robotic decision-making and arm control. IFMRTs maintain their reconfigurability even after 5000 bending cycles at 4 mm radius. The design and demonstration of IFMRTs open up possibilities for flexible wearable electronics and soft intelligent robots.

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Publication Details

Journal
Nature Communications
Published
2025-12-09
DOI
https://doi.org/10.1038/s41467-025-67041-8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Intrinsically flexible multimode reconfigurable transistors for polymorphic circuits and neuromorphic devices

Sixin Zhang, Qiuyue Huang, Zifan Wang, Jie Liang et al.
Nature Communications
Advanced Memory and Neural Computing
article

Intrinsically flexible multimode reconfigurable transistors for polymorphic circuits and neuromorphic devices

Sixin Zhang, Qiuyue Huang, Zifan Wang, Jie Liang, Dexing Liu, Tianyu Zhu, Wanting Wang, Xinwei Wang, Min Zhang, Jialiang Wang, Jiahao Zhu, Rui Qiu, Chunxiu Wang
article en

Abstract

Reconfigurable transistors hold considerable significance for both integrated circuits and neuromorphic electronics. At the same time, flexible electronics is developing rapidly, promoted by various emerging applications, such as wearable electronics and smart robots. Therefore, flexible reconfigurable transistors are highly expected to create emerging application scenarios while they are seldom reported. Here, intrinsically flexible multimode reconfigurable transistors (IFMRTs) with dual-gate structure are proposed and realized. Either top or bottom gate can serve as the modulation terminal to switch the transistor among p-type, n-type, and ambipolar modes. The threshold voltage of the reconfigurable transistor is modulable. Based on IFMRTs, we have demonstrated inverters and polymorphic logic circuits with key bit-selectable NAND or NOR logic functions, which provides solutions for hardware security. We have also demonstrated artificial heterosynapse and dendrite, achieving reconfigurable synaptic responses and dendrite integration. Taking the simulation of automatic obstacle avoidance and coordination control of hand movement by low-level and high-level neural centers as examples, we illustrate the potential intelligence of IFMRTs in robotic decision-making and arm control. IFMRTs maintain their reconfigurability even after 5000 bending cycles at 4 mm radius. The design and demonstration of IFMRTs open up possibilities for flexible wearable electronics and soft intelligent robots.

Nature CommunicationsVol. 17(1)
Peking University (CN), Chinese University of Hong Kong, Shenzhen (CN), Peking University Shenzhen Hospital (CN)
National Natural Science Foundation of China, Chinese University of Hong Kong, Shenzhen University
Openalex Percentile: Top 60%
Advanced Memory and Neural Computing
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