RTN in Scaled Transistors for On-Chip Random Seed Generation
RTN in Scaled Transistors for On-Chip Random Seed Generation
复制标题
DOI：
10.1109/tvlsi.2017.2687762
复制
发表时间：
2017-08-01
影响因子：
2.8
通讯作者：
Cao, Yu
中科院分区：
文献类型：
--
作者：
Mohanty, Abinash;Sutaria, Ketul B.;Cao, Yu
随机数在密码学中起着至关重要的作用，它们用于生成密钥、随机数、一次性密码本和对称加密的初始化向量。随机数发生器（RNG）的质量对这些算法的脆弱性和性能有着重要的影响。伪RNG使用确定性算法来产生分布非常类似于均匀分布的数字。另一方面，真正的RNG（TRNG）使用一些自然现象/过程来生成随机比特。它们是不确定的，因为下一个要生成的数字无法提前确定。在本文中，一种新的片上噪声源，随机电报噪声（RTN），是简单和可靠的TRNG开发。RTN是一种随机捕获/释放电荷的微观过程，通常被认为是一种噪声，并在设计中加以缓解。通过物理建模和硅测量，我们证明了RTN适用于TRNG，特别是在高度缩放的MOSFET中。由于RTN的速度较慢，我们的目的是为随机数的片上种子产生系统。我们的贡献是：1）RTN的物理模型校准，具有全面的65-和180-nm晶体管测量; 2）RTN的缩放趋势，用低至28 nm的硅数据进行验证; 3）通过使用固有RTN物理特性实现50%信号概率的设计原理，无需传统的后处理算法，生成的序列通过美国国家标准与技术研究所（NIST）测试;以及4）解决实际问题的解决方案，包括多电平RTN信号，对电压和温度波动的鲁棒性以及操作速度。
Random numbers play a vital role in cryptography, where they are used to generate keys, nonce, one-time pads, and initialization vectors for symmetric encryption. The quality of random number generator (RNG) has significant implications on vulnerability and performance of these algorithms. A pseudo-RNG uses a deterministic algorithm to produce numbers with a distribution very similar to uniform. True RNGs (TRNGs), on the other hand, use some natural phenomenon/process to generate random bits. They are nondeterministic, because the next number to be generated cannot be determined in advance. In this paper, a novel on-chip noise source, random telegraph noise (RTN), is exploited for simple and reliable TRNG. RTN, a microscopic process of stochastic trapping/detrapping of charges, is usually considered as a noise and mitigated in design. Through physical modeling and silicon measurement, we demonstrate that RTN is appropriate for TRNG, especially in highly scaled MOSFETs. Due to the slow speed of RTN, we purpose the system for on-chip seed generation for random number. Our contributions are: 1) physical model calibration of RTN with comprehensive 65- and 180-nm transistor measurements; 2) the scaling trend of RTN, validated with silicon data down to 28 nm; 3) design principles to achieve 50% signal probability by using intrinsic RTN physical properties, without traditional postprocessing algorithms, the generated sequence passes the National Institute of Standards and Technology (NIST) tests; and 4) solutions to manage realistic issues in practice, including multilevel RTN signal, robustness to voltage and temperature fluctuations and the operation speed.