Direct photo-patterning of halide perovskites toward machine-learning-assisted erasable photonic cryptography
Yingjie Zhao,
Mengru Zhang,
Zhaokai Wang,
Haoran Li,
Yi Hao,
Yu Chen,
Lei Jiang,
Yuchen Wu (),
Shuang-Quan Zang () and
Yanlin Song ()
Additional contact information
Yingjie Zhao: Zhengzhou University
Mengru Zhang: Zhengzhou University
Zhaokai Wang: Zhengzhou University
Haoran Li: Zhengzhou University
Yi Hao: Zhengzhou University
Yu Chen: Chinese Academy of Sciences
Lei Jiang: Chinese Academy of Sciences
Yuchen Wu: Chinese Academy of Sciences
Shuang-Quan Zang: Zhengzhou University
Yanlin Song: Zhengzhou University
Nature Communications, 2025, vol. 16, issue 1, 1-11
Abstract:
Abstract The patterning of perovskites is significant for optical encryption, display, and optoelectronic integrated devices. However, stringent and complex fabrication processes restrict its development and applications. Here, we propose a conceptual methodology to realize erasable patterns based on binary mix-halide perovskite films via a direct photo-patterning technique. Controllable ion migration and photochemical degradation mechanism of iodine-rich regions ensure high-fidelity photoluminescence images with different patterns, sizes, and fast self-erasure time within 5 seconds, yielding erasable photonic cryptography chip, which guarantees the efficient transmission of confidential information and avoids the secondary leakage of information. The ultrafast information encryption, decryption, and erasable processes are attributed to the modulation of the crystallographic orientation of the perovskite film, which lowers the ion migration activation energy and accelerates the ion migration rate. Neural network-assisted multi-level pattern encoding technology with high accuracy and efficiency further enriches the content of the transmitted information and increases the security of the information. This pioneering work provides a strategy and opportunity for the integration of erasable photonic patterning devices based on perovskite materials.
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.nature.com/articles/s41467-025-58677-7 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58677-7
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-025-58677-7
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().