2022 International Conference on Solid State Devices and Materials

講演情報

Oral Presentation

Focus Session 2 (Area1&2&8)

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

2022年9月29日(木) 13:30 〜 15:15 201 (2F)

Session Chair: Toshifumi Irisawa (AIST), Hiroshi Naganuma (Tohoku Univ.)

14:15 〜 14:30

[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 Ang1,2 (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.

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