Bi2O2Se-Based True Random Number Generator for Security Applications

The fast development of the Internet of things (IoT) promises to deliver convenience to human life. However, a huge amount of the data is constantly generated, transmitted, processed, and stored, posing significant security challenges. The currently available security protocols and encryption techni...

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Veröffentlicht in:ACS nano 2022-04, Vol.16 (4), p.6847-6857
Hauptverfasser: Liu, Bo, Chang, Ying-Feng, Li, Juzhe, Liu, Xu, Wang, Le An, Verma, Dharmendra, Liang, Hanyuan, Zhu, Hui, Zhao, Yudi, Li, Lain-Jong, Hou, Tuo-Hung, Lai, Chao-Sung
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container_end_page 6857
container_issue 4
container_start_page 6847
container_title ACS nano
container_volume 16
creator Liu, Bo
Chang, Ying-Feng
Li, Juzhe
Liu, Xu
Wang, Le An
Verma, Dharmendra
Liang, Hanyuan
Zhu, Hui
Zhao, Yudi
Li, Lain-Jong
Hou, Tuo-Hung
Lai, Chao-Sung
description The fast development of the Internet of things (IoT) promises to deliver convenience to human life. However, a huge amount of the data is constantly generated, transmitted, processed, and stored, posing significant security challenges. The currently available security protocols and encryption techniques are mostly based on software algorithms and pseudorandom number generators that are vulnerable to attacks. A true random number generator (TRNG) based on devices using stochastically physical phenomena has been proposed for auditory data encryption and trusted communication. In the current study, a Bi2O2Se-based memristive TRNG is demonstrated for security applications. Compared with traditional metal–insulator–metal based memristors, or other two-dimensional material-based memristors, the Bi2O2Se layer as electrode with non-van der Waals interface, high carrier mobility, air stability, extreme low thermal conductivity, as well as vertical surface resistive switching shows intrinsic stochasticity and complexity in a memristive true analogue/digital random number generation. Moreover, those analogue/digital random number generation processes are proved to be resilient for machine learning prediction.
doi_str_mv 10.1021/acsnano.2c01784
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title Bi2O2Se-Based True Random Number Generator for Security Applications
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