Three‐Terminal Reconfigurable Volatile/Nonvolatile Light‐Emitting Memristor for Integrated Neuromorphic Display Systems

Reconfigurable volatile/nonvolatile neuromorphic devices integrate brain-like rapid learning with stable memory, providing a critical pathway toward edge-intelligent systems. The integration of reconfigurable devices and light-emitting functionality transforms the display from a passive output terminal into an integral hub of the intelligent system. However, integrating reconfigurable volatile/nonvolatile memory and light-emitting functionality within a single device remains a major challenge because of the inherent conflicts among multiple functions and the constraints of conventional two-terminal control. Here, for the first time, we present a three-terminal reconfigurable volatile/nonvolatile light-emitting memristor. The three-terminal configuration provides control of the emission region and memory states, enabling seamless transition between volatile, nonvolatile, and light-emission operations. By enabling spatial reconfigurability and temporal multiplexing of emission, the device improves scalability and simplifies system architecture and control, achieving a 69.23% reduction in data-transfer volume and a 40% reduction in system component count compared with separated architectures. In addition, it offers direct visual feedback while facilitating continuous learning in neuromorphic computing systems. Finally, we implement an anomaly-detection visualization system based on the reconfigurable volatile/nonvolatile light-emitting memristor, demonstrating its ability to produce direct visual output while processing information, thus enabling an integrated memory-compute-display architecture for next-generation edge-intelligent display systems.

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Publication Details

Journal
Advanced Materials
Published
2026-05-01
DOI
https://doi.org/10.1002/adma.73239
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Three‐Terminal Reconfigurable Volatile/Nonvolatile Light‐Emitting Memristor for Integrated Neuromorphic Display Systems

Yi Zou, Gengxu Chen, Yan Xu, Jiabin Ye et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Three‐Terminal Reconfigurable Volatile/Nonvolatile Light‐Emitting Memristor for Integrated Neuromorphic Display Systems

Yi Zou, Gengxu Chen, Yan Xu, Jiabin Ye, Chuiying Yang, Huipeng Chen, Peng Yang, Shengji Yang
article en

Abstract

Reconfigurable volatile/nonvolatile neuromorphic devices integrate brain-like rapid learning with stable memory, providing a critical pathway toward edge-intelligent systems. The integration of reconfigurable devices and light-emitting functionality transforms the display from a passive output terminal into an integral hub of the intelligent system. However, integrating reconfigurable volatile/nonvolatile memory and light-emitting functionality within a single device remains a major challenge because of the inherent conflicts among multiple functions and the constraints of conventional two-terminal control. Here, for the first time, we present a three-terminal reconfigurable volatile/nonvolatile light-emitting memristor. The three-terminal configuration provides control of the emission region and memory states, enabling seamless transition between volatile, nonvolatile, and light-emission operations. By enabling spatial reconfigurability and temporal multiplexing of emission, the device improves scalability and simplifies system architecture and control, achieving a 69.23% reduction in data-transfer volume and a 40% reduction in system component count compared with separated architectures. In addition, it offers direct visual feedback while facilitating continuous learning in neuromorphic computing systems. Finally, we implement an anomaly-detection visualization system based on the reconfigurable volatile/nonvolatile light-emitting memristor, demonstrating its ability to produce direct visual output while processing information, thus enabling an integrated memory-compute-display architecture for next-generation edge-intelligent display systems.

Advanced Materials
Chinese Academy of Sciences (CN), Technical Institute of Physics and Chemistry (CN), Tan Kah Kee Innovation Laboratory (CN), Fuzhou University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
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