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Nano-intrinsic true random number generation: a device to data study

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dc.contributor.authorKim, J.
dc.contributor.authorNili, H.
dc.contributor.authorTruong, N.D.
dc.contributor.authorAhmed, T.
dc.contributor.authorYang, J.
dc.contributor.authorJeong, D.S.
dc.contributor.authorSriram, S.
dc.contributor.authorRanasinghe, D.C.
dc.contributor.authorIppolito, S.
dc.contributor.authorChun, H.
dc.contributor.authorKavehei, O.
dc.date.issued2019
dc.description.abstractWe present a circuit technique to extract true random numbers from carrier capture and emission in oxide traps in the emerging redox-based resistive memory (ReRAM). This phenomenon that appears as small changes in current magnitude passing through the device is known as random telegraph noise (RTN) and is increasingly becoming a source of reliability issues in nanometer-scale devices. We demonstrate a circuit that exploits TRN suitable for a true random number generator (TRNG) in security applications, where the system is secure from different adversarial attacks, including side-channel monitoring and machine learning analysis. We experimentally characterize RTN in ReRAMs and extract its dependency to temperature, voltage, and area. We introduce an RTN harvesting circuit to mitigate sensitivities to temperature fluctuations, injected supply noise, and power signal monitoring. We reduced bias and imbalance in data due to high-speed sampling via von Neumann whitening. The circuit is compared to conventional non-differential readout approach. Our approach shows a 7.26 times improvement in autocorrelation and significant resilience against the injected supply noise. We also demonstrate the TRNG's quality and robustness using statistical tests and machine learning attacks. The output of the generator satisfies statistical tests for randomness and is immune to modeling attacks based on the machine learning methods.
dc.description.statementofresponsibilityJeeson Kim, Hussein Nili, Nhan Duy Truong, Taimur Ahmed, Jiawei Yang, Doo Seok Jeong, Sharath Sriram, Damith C. Ranasinghe, Samuel Ippolito, Hosung Chun, and Omid Kavehei
dc.identifier.citationIEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2019; 66(7):2615-2626
dc.identifier.doi10.1109/TCSI.2019.2895045
dc.identifier.issn1549-8328
dc.identifier.issn1558-0806
dc.identifier.orcidRanasinghe, D.C. [0000-0002-2008-9255]
dc.identifier.urihttp://hdl.handle.net/2440/120947
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140103448
dc.rights© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
dc.source.urihttps://doi.org/10.1109/tcsi.2019.2895045
dc.subjectLow-frequency noise; non-volatile memory; random number generation; random telegraph noise; resistive memory
dc.titleNano-intrinsic true random number generation: a device to data study
dc.typeJournal article
pubs.publication-statusPublished

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