Stochastic Photoresponse‐Driven Perovskite TRNGs for Secure Encryption Systems
True random numbers are essential for ensuring information security and supporting simulations across various industries. With the exponential growth of data driven by advancements in artificial intelligence, robust encryption for communications has become increasingly important. While software-based deterministic random number algorithms are cost-effective and easy to use, they are vulnerable to attacks by powerful supercomputers, highlighting the need for more secure alternatives. As portable electronic devices and information-gathering sensors proliferate, portable true random number generators (TRNGs) are critical for maintaining security. In this work, hybrid material-based photodetectors composed of anionic polymers and perovskites that maximize stochastic photogeneration for TRNG applications are presented. By integrating perovskite photodetectors with simple electronic circuits, compact, low-power TRNG devices have been developed that are versatile and resilient to environmental factors. These devices generate 10 000 bits s<sup>-1</sup> without resets or delays, achieving significant miniaturization. The generated 10 Mbit random number is validated through US National Institute of Standards and Technology (NIST) testing. Using a 480 000-bit random sequence, perfect image encryption, ensuring protection against hacking are demonstrated. Additionally, the perovskite TRNG can operate under external light even when embedded in pork skin, realizing its potential as an implantable device for personal security and authentication.
Authors
- Hyun Seok Song (ORCID: https://orcid.org/0000-0002-5104-4033)
- Heon Lee (ORCID: https://orcid.org/0000-0003-1964-3746)
- Gun Young Jung (ORCID: https://orcid.org/0000-0003-1163-8651)
- Yeon Kyung Lee (ORCID: https://orcid.org/0009-0000-2563-7110)
- Yusin Pak (ORCID: https://orcid.org/0000-0002-5444-6513)
- Minah Seo (ORCID: https://orcid.org/0000-0003-1290-9716)
- Minz Lee
- Yohan Yoon (ORCID: https://orcid.org/0000-0002-9138-9677)
- Jung‐Wook Min (ORCID: https://orcid.org/0000-0002-7570-1739)
- Jun Young Lee (ORCID: https://orcid.org/0000-0002-8403-9935)
- Dante Ahn
- Woochul Kim (ORCID: https://orcid.org/0000-0003-3966-8414)
- Hangyeol Choi
Institutions
- Kumoh National Institute of Technology (KR)
- Korea University (KR)
- Samsung (South Korea) (KR)
- Korea Aerospace University (KR)
- Gwangju Institute of Science and Technology (KR)
- Korea Institute of Science and Technology (KR)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1002/advs.202412139
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 3.65
Funders
- Korea Institute of Science and Technology
- National Research Foundation of Korea
- National Research Council of Science and Technology