Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/120947
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Type: Journal article
Title: Nano-intrinsic true random number generation: a device to data study
Author: Kim, J.
Nili, H.
Truong, N.
Ahmed, T.
Yang, J.
Jeong, D.
Sriram, S.
Ranasinghe, D.
Ippolito, S.
Chun, H.
Kavehei, O.
Citation: IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2019; 66(7):2615-2626
Publisher: IEEE
Issue Date: 2019
ISSN: 1549-8328
1558-0806
Statement of
Responsibility: 
Jeeson 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
Abstract: We 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.
Keywords: Low-frequency noise; non-volatile memory; random number generation; random telegraph noise; resistive memory
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.
DOI: 10.1109/TCSI.2019.2895045
Grant ID: http://purl.org/au-research/grants/arc/DP140103448
Appears in Collections:Aurora harvest 8
Chemical Engineering publications

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