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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

Multi-layer 3-dimensional (3D) vertical RRAM (VRRAM) PUF with in-cell stabilization scheme to improve both cost efficiency and reliability. The proposed PUF features excellent resistance against machine learning attacks.

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Abstract:
“In 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 >10 7 . 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 >7X (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.”

Find the technical paper link here (IEEE Xplore) or here (Research Gate where you can request a copy directly from the authors).  Published 03/2021.

J. Yang et al., “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,” 2020 IEEE International Electron Devices Meeting (IEDM), 2020, pp. 28.6.1-28.6.4, doi: 10.1109/IEDM13553.2020.9372107.



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