Nano-Intrinsic True Random Number Generation: A Device to Data Study
Nano-Intrinsic True Random Number Generation: A Device to Data Study
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DOI：
10.1109/tcsi.2019.2895045
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发表时间：
2019-07-01
影响因子：
5.1
通讯作者：
Kavehei, Omid
中科院分区：
文献类型：
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作者：
Kim, Jeeson;Nili, Hussein;Kavehei, Omid
我们提出了一种电路技术，可以从新兴的基于氧化还原的电阻存储器（ReRAM）中的氧化物陷阱中的载流子捕获和发射中提取真实的随机数。这种现象表现为通过设备的电流大小发生微小变化，称为随机电报噪声 (RTN)，并且日益成为纳米级设备可靠性问题的根源。我们演示了一种利用 TRN 的电路，该电路适用于安全应用中的真随机数生成器 (TRNG)，其中系统可以免受不同的对抗性攻击，包括侧信道监控和机器学习分析。我们通过实验表征 ReRAM 中的 RTN，并提取其对温度、电压和面积的依赖性。我们引入了 RTN 采集电路来减轻对温度波动、注入电源噪声和电源信号监控的敏感性。我们通过冯诺依曼白化进行高速采样，减少了数据的偏差和不平衡。该电路与传统的非差分读出方法进行了比较。我们的方法显示自相关性提高了 7.26 倍，并且对注入的电源噪声具有显着的恢复能力。我们还使用统计测试和机器学习攻击来证明 TRNG 的质量和稳健性。生成器的输出满足随机性统计测试，并且不受基于机器学习方法的建模攻击。
We present a circuit technique to extract true random numbers from carrier capture and emission in oxide traps in the emerging redox-based resistive memory (ReRAM). This phenomenon that appears as small changes in current magnitude passing through the device is known as random telegraph noise (RTN) and is increasingly becoming a source of reliability issues in nanometer-scale devices. We demonstrate a circuit that exploits TRN suitable for a true random number generator (TRNG) in security applications, where the system is secure from different adversarial attacks, including side-channel monitoring and machine learning analysis. We experimentally characterize RTN in ReRAMs and extract its dependency to temperature, voltage, and area. We introduce an RTN harvesting circuit to mitigate sensitivities to temperature fluctuations, injected supply noise, and power signal monitoring. We reduced bias and imbalance in data due to high-speed sampling via von Neumann whitening. The circuit is compared to conventional non-differential readout approach. Our approach shows a 7.26 times improvement in autocorrelation and significant resilience against the injected supply noise. We also demonstrate the TRNG's quality and robustness using statistical tests and machine learning attacks. The output of the generator satisfies statistical tests for randomness and is immune to modeling attacks based on the machine learning methods.