Liu Yang, Liu Pengyu, Wen Yingyi, Liang Zihan, Liu Songwei, Song Lekai, Pei Jingfang, Fan Xiaoyue, Ma Teng, Wang Gang, Gao Shuo, Pun Kong-Pang, Chen Xiaolong, Hu Guohua
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR 999077, China.
Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR 999077, China.
Nano Lett. 2024 Nov 13;24(45):14315-14322. doi: 10.1021/acs.nanolett.4c03957. Epub 2024 Nov 1.
True random numbers are crucial for various research and engineering problems. Their generation depends upon a robust physical entropy noise. Here, we present true random number generation from the conductance noise probed in structurally metastable 1T' molybdenum ditelluride (MoTe). The noise, fitting a Poisson process, is proved to be a robust physical entropy noise at low and even cryogenic temperatures. Noise characteristic analyses suggest the noise may originate from the polarization variations of the underlying ferroelectric dipoles in 1T' MoTe. We demonstrate the noise allows for true random number generation, and this facilitates their use as the seed for generating high-throughput secure random numbers exceeding 1 Mbit/s, appealing for practical applications in, for instance, cryptography where data security is now critical. As an example, we show biometric information safeguarding in neural networks by using the random numbers as the mask, proving a promising data security measure in big data and artificial intelligence.
真正的随机数对于各种研究和工程问题至关重要。它们的生成依赖于强大的物理熵噪声。在此，我们展示了从结构亚稳的1T'二碲化钼（MoTe₂）中探测到的电导噪声生成真正的随机数。这种拟合泊松过程的噪声在低温甚至极低温下被证明是一种强大的物理熵噪声。噪声特性分析表明，该噪声可能源于1T' MoTe₂中底层铁电偶极子的极化变化。我们证明这种噪声可用于生成真正的随机数，这有助于将其用作生成超过1 Mbit/s的高通量安全随机数的种子，在诸如密码学等数据安全至关重要的实际应用中具有吸引力。例如，我们展示了通过使用随机数作为掩码在神经网络中保护生物特征信息，证明了在大数据和人工智能中一种有前景的数据安全措施。
