Nano-Intrinsic True Random Number Generation: A Device to Data Study

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)...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2019-07, Vol.66 (7), p.2615-2626
Main Authors: Kim, Jeeson, Nili, Hussein, Truong, Nhan Duy, Ahmed, Taimur, Yang, Jiawei, Jeong, Doo Seok, Sriram, Sharath, Ranasinghe, Damith C., Ippolito, Samuel, Chun, Hosung, Kavehei, Omid
Format: Article
Language:English
Subjects:
Artificial intelligence
Australia
Circuit reliability
Entropy
Field effect transistors
Generators
Low-frequency noise
Machine learning
Nanoscale devices
Noise
Noise monitoring
non-volatile memory
Numbers
random number generation
Random numbers
random telegraph noise
resistive memory
Signal monitoring
Statistical tests
Switches
Temperature dependence
Variations
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Summary: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.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2019.2895045