Visible to the public A Machine-Learning-Resistant 3D PUF with 8-layer Stacking Vertical RRAM and 0.014% Bit Error Rate Using In-Cell Stabilization Scheme for IoT Security Applications

TitleA Machine-Learning-Resistant 3D PUF with 8-layer Stacking Vertical RRAM and 0.014% Bit Error Rate Using In-Cell Stabilization Scheme for IoT Security Applications
Publication TypeConference Paper
Year of Publication2020
AuthorsYang, Jianguo, Lei, Dengyun, Chen, Deyang, Li, Jing, Jiang, Haijun, Ding, Qingting, Luo, Qing, Xue, Xiaoyong, Lv, Hangbing, Zeng, Xiaoyang, Liu, Ming
Conference Name2020 IEEE International Electron Devices Meeting (IEDM)
Date PublishedDec. 2020
PublisherIEEE
ISBN Number978-1-7281-8888-1
Keywordscomposability, Metrics, Microelectronics, NIST, pubcrawl, reliability, resilience, Resiliency, security, Temperature measurement, Testing, Three-dimensional displays, Tin, Training
AbstractIn this work, we propose and demonstrate a multi-layer 3-dimensional (3D) vertical RRAM (VRRAM) PUF with in-cell stabilization scheme to improve both cost efficiency and reliability. An 8-layer VRRAM array was manufactured with excellent uniformity and good endurance of \textbackslashtextgreater107. Apart from the variation in RRAM resistance, enhanced randomness is obtained thanks to the parasitic IR drop and abundant sneak current paths in 3D VRRAM. To deal with the common issue of unstable bits in PUF output, in-cell stabilization is proposed by first employing asymmetric biasing to detect the unstable bits and then exploiting reprogramming to expand the deviation to stabilize the output. The bit error rate is reduced by \textbackslashtextgreater7X (68X) for 3(5) times reprogramming. The proposed PUF features excellent resistance against machine learning attack and passes both National Institute of Standards and Technology (NIST) 800-22 and NIST 800-90B test suites.
URLhttps://ieeexplore.ieee.org/document/9372107
DOI10.1109/IEDM13553.2020.9372107
Citation Keyyang_machine-learning-resistant_2020