A Single OLED With Hybrid Synaptic Plasticity Enabling In Situ Neuromorphic Processing and Visualization
As the core interface for human-computer interaction, multitask intelligent display systems face increasingly stringent demands regarding precision and functional integration. However, in existing architectures, light-emitting units only act as passive display terminals. Endowing light-emitting devices with computing capabilities and realizing computing-as-display, thereby reducing transmission overhead and improving display precision, is the key to promoting the further development of this field. Inspired by the human brain, we innovatively report an organic light-emitting diode featuring both long- and short-term synaptic plasticity and synaptic weight-driven electroluminescence. By engineering charge-trapping structures and heterojunction potential wells within a single OLED device, we establish dual-tier synaptic response output channels for information processing, operating independently on second- and millisecond-level timescales under electrical and optical signal modulation, respectively. Following information processing, the processed results are visualized via a globally driven electroluminescence mechanism enabled by synaptic weight modulation. We introduce the Multi-Task Display Fidelity index as a universal metric to quantify the cooperative accuracy between processing and display, achieving a value of 90.86%. Furthermore, the processing-display coupling index of the device surpasses the corresponding index of mainstream CMOS architectures by 67.8%. This work demonstrates the immense potential of hybrid synaptic plasticity OLEDs for multitask intelligent display.
Authors
- Huipeng Chen (ORCID: https://orcid.org/0000-0003-1706-3174)
- Li J (ORCID: https://orcid.org/0000-0002-6679-5227)
- Zhenjia Chen (ORCID: https://orcid.org/0009-0006-4139-6103)
- Shiyang Sun
- Chuiying Yang
- Shengji Yang
- Peidong Kang (ORCID: https://orcid.org/0009-0000-8748-7007)
- Xin Ai
Institutions
- Tan Kah Kee Innovation Laboratory (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2026-07-23
- DOI
- https://doi.org/10.1002/adma.74244
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China