[F-9] CMOS and Memory Applications of Low Dimensional Materials I

[F-9-03] Machine Learning Attack Resilient MoS₂ Fe-FET True Random Number Generator for Hardware Security in IoT: 0.7 pJ/bit Writing Energy, Self-Correction Function, and 1250 bit/s Seed Throughput

〇Yu-Chieh Chien¹, Heng Xiang¹, Jianze Wang¹, Yufei Shi¹, Xuanyao Fong¹, Kah-Wee Ang^1,2 (1. National Univ. of Singapore (Singapore), 2. Inst. of Materials Res. and Eng., A*STAR (Singapore))

Presentation style: On-site (in-person)

https://doi.org/10.7567/SSDM.2022.F-9-03

We demonstrate a machine learning attack resilient true random number generator (TRNG) based on MoS2 Fe-FET. The probabilistic nature of the ferroelectric-accelerated charge trapping/de-trapping mechanism is exploited as the entropy source to achieve (i) a significantly reduced operation voltage to trigger the stochastic process by introducing a ferroelectric HZO layer with a writing energy (Ewrite) of ~0.7 pJ/bit; (ii) a physically unclonable Fe-TRNG with a near-ideal entropy of 0.99; (iii) a designed self-correction circuitry that further improves its randomness with no observable degradation after 3×106 endurance cycles. Additionally, we demonstrate the random quick response (QR) passwords as a protection layer for enhancing the hardware security in IoT. A seed throughput of 1250 bit/s and an estimated circuit power consumption of ~0.37 mW are featured in the Fe-TRNG array.