Author
Listed:
- Yifei Zhao
(The Hong Kong Polytechnic University)
- Man Li
(South China University of Technology)
- Man Chung Wong
(The Hong Kong Polytechnic University)
- Xun Han
(The Hong Kong Polytechnic University)
- Feng Guo
(The Hong Kong Polytechnic University
The Hong Kong Polytechnic University Shenzhen Research Institute)
- Yuan Liu
(The Hong Kong Polytechnic University)
- Xinyue Lao
(The Hong Kong Polytechnic University)
- Zhaoying Dang
(The Hong Kong Polytechnic University
The Hong Kong Polytechnic University Shenzhen Research Institute)
- Sin-Yi Pang
(The Hong Kong Polytechnic University)
- Zehan Wu
(The Hong Kong Polytechnic University)
- Shi Ye
(South China University of Technology)
- Jianhua Hao
(The Hong Kong Polytechnic University
The Hong Kong Polytechnic University Shenzhen Research Institute
The Hong Kong Polytechnic University)
Abstract
The unprecedented growth in information across diverse media drives an urgent need for multifunctional materials and devices beyond conventional electrical paradigms. This work explores all-optical information processing based on photoluminescence functions using smart phosphor. The developed composite phosphor of mixed-halide perovskite embedded macroporous Y2O3:Eu3+ exhibits adaptive photoluminescence variations with neuromorphic characteristics. Theoretical simulations reveal interface-mediated halogen migration processes with progressively evolving energy barriers, underpinning the neuron-like photoluminescence property variations. The system enables full photoluminescence-based Write and Read functionalities for all-optical neuromorphic computing, achieving 4-bit binary sequence discrimination as physical reservoirs. It further demonstrates potential in photoluminescence-based fingerprint authentication with 94.4% accuracy. This work advances smart phosphor as an alternative approach to neuromorphic computing with optical-stimuli and optical-output. It also opens avenues for designing function-oriented phosphor materials with tailored properties for information science and artificial intelligence applications.
Suggested Citation
Yifei Zhao & Man Li & Man Chung Wong & Xun Han & Feng Guo & Yuan Liu & Xinyue Lao & Zhaoying Dang & Sin-Yi Pang & Zehan Wu & Shi Ye & Jianhua Hao, 2025.
"Smart phosphor with neuromorphic behaviors enabling full-photoluminescent Write and Read for all-optical physical reservoir computing,"
Nature Communications, Nature, vol. 16(1), pages 1-11, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62745-3
DOI: 10.1038/s41467-025-62745-3
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62745-3. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.