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.
Authors
- Yi Zou (ORCID: https://orcid.org/0000-0002-7161-3758)
- Gengxu Chen (ORCID: https://orcid.org/0000-0001-8064-7645)
- Yan Xu (ORCID: https://orcid.org/0000-0001-6576-2389)
- Jiabin Ye
- Chuiying Yang
- Huipeng Chen
- Peng Yang
- Shengji Yang
Institutions
- Chinese Academy of Sciences (CN)
- Technical Institute of Physics and Chemistry (CN)
- Tan Kah Kee Innovation Laboratory (CN)
- Fuzhou University (CN)
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
Funders
- National Natural Science Foundation of China