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
Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, People's Republic of China.
Artificial Intelligence Research Center, Chang Gung University, Guishan District, 33302 Taoyuan, Taiwan.
ACS Nano. 2022 Apr 26;16(4):6847-6857. doi: 10.1021/acsnano.2c01784. Epub 2022 Mar 25.
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 BiOSe-based memristive TRNG is demonstrated for security applications. Compared with traditional metal-insulator-metal based memristors, or other two-dimensional material-based memristors, the BiOSe 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.
物联网（IoT）的快速发展有望为人类生活带来便利。然而，大量数据不断产生、传输、处理和存储，带来了重大的安全挑战。目前可用的安全协议和加密技术大多基于易受攻击的软件算法和伪随机数生成器。一种基于利用随机物理现象的设备的真随机数发生器（TRNG）已被提出用于听觉数据加密和可信通信。在当前研究中，展示了一种基于BiOSe的忆阻式TRNG用于安全应用。与传统的基于金属-绝缘体-金属的忆阻器或其他基于二维材料的忆阻器相比，作为电极的BiOSe层具有非范德华界面、高载流子迁移率、空气稳定性、极低的热导率以及垂直表面电阻开关特性，在忆阻式真模拟/数字随机数生成中表现出内在的随机性和复杂性。此外，这些模拟/数字随机数生成过程被证明对机器学习预测具有弹性。
