Ferroelectrically programmable lanthanide luminescent memristor
Spatial-light computing requires rewritable, multilevel and persistent optical weights, but many implementations rely on volatile modulators or static power. Here we report a CMOS-imaged luminescent memristor array in which ferroelectric domain switching in Er/Yb-doped PMN-PT single crystals programs non-volatile photoluminescence (PL) states. Domain reconfiguration tunes the local crystal-field symmetry of lanthanide emitters, enabling 16 analogue levels, retention over 27 h, endurance beyond 105 cycles and microsecond-scale state programming with zero electrical standby power for state retention. An 8 × 8 array, addressed by a diffractive optical element and read by a proximal CMOS sensor, converts stored emissive states into a single-frame intensity map and achieves 94.02% pixel-wise state recognition using calibration-aware decoding. The array implements single-step optical linear weighting, while the same decoded weights support hybrid optical-electrical handwritten-digit inference approaching a 32-bit floating-point software baseline. These results establish a non-volatile, image-addressable emissive weight element for photonic computing. Spatial-light computing is limited by the lack of compact, non-volatile, rewritable analog devices. Wen et al. report a ferroelectric crystal that converts lanthanide emission into a rewritable, multilevel memory, allowing stored optical states to be read and used as persistent weights for neuromorphic computing.
Authors
- Wenzheng Ma
- Ziyun Chen (ORCID: https://orcid.org/0000-0001-7294-5722)
- Yiyang Wen (ORCID: https://orcid.org/0009-0006-6433-1679)
- Jianhua Hao (ORCID: https://orcid.org/0000-0002-6186-5169)
- Yuhao Feng (ORCID: https://orcid.org/0000-0002-1720-9084)
- Zhenping Wu (ORCID: https://orcid.org/0000-0003-2986-8068)
- Xu Li (ORCID: https://orcid.org/0000-0001-5221-0817)
- Yang Zhang (ORCID: https://orcid.org/0000-0002-9840-1755)
- Weiwei Liu (ORCID: https://orcid.org/0000-0001-6036-9641)
- Minghao Hu (ORCID: https://orcid.org/0000-0002-2222-6823)
- Xiaona Du (ORCID: https://orcid.org/0009-0005-2622-7435)
- Yilin Cao
- Hongda Ren
- Fan Zhang
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Hong Kong Polytechnic University (HK)
- Nankai University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Shanghai Institute of Ceramics (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-09-08
- DOI
- https://doi.org/10.1038/s41467-026-77397-0
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China