Unified In-Memory Dynamic TRNG and Multi-Bit Static PUF Entropy Generation for Ubiquitous Hardware Security including PUF responses lying at bin boundaries (affecting masking only marginally, see BER below).

📋 论文概要

该论文提出了一种统一的存内动态真随机数发生器（TRNG）和多比特静态物理不可克隆函数（PUF）熵生成架构，用于普适硬件安全。通过共享存储单元实现TRNG和PUF功能，在0.8-1V供电和-10至75°C温度范围内，TRNG的最小熵大于0.99，通过所有NIST测试，实现了密码级随机性。

💡 主要创新点

• 首次提出统一的存内动态TRNG和多比特静态PUF架构，共享存储单元以降低面积开销。
• 通过动态和静态熵源结合，在宽电压和温度范围内保持高随机性和稳定性，无需额外校准。
• 实现了多比特PUF输出，提高了单位面积的熵生成效率。

🏷 关键词

📄 原文摘要

In Fig. 36.1.4, the TRNG was confirmed to have consistent measured output quality across very different data patterns (all 0’s for minimum jitter vs. random data), 0.8-to-1V supply and -10 to 75°C temperature. The min-entropy is always greater than 0.99, all NIST tests passed (p-value>0.01), autocorrelation function (ACF) at 95% confidence is within 0.002, and the phi coefficient between simultaneous streams is near-zero (0.001 on average), confirming cryptographic-grade randomness. Under 1b Von Neumann extraction (6000F2, off-chip) and 1 dropped LSB, ~2.25 random bits are generated by every column at 36,000F2 area overhead. TRNG operation maintains nearlyconstant energy across temperatures thanks to the GRO frequency tuning loop, reducing energy variability from 5× to 2.3× (Fig. 36.1.4). National University of Singapore, Singapore, Singapore Secure integrated systems routinely require the generation of keys in the form of

👥 作者与机构

Sachin Taneja, Viveka Konandur Rajanna, Massimo Alioto