A novel true random number generator based on a stochastic diffusive memristor

Hao Jiang, Daniel Belkin, Sergey E. Savel’ev, Siyan Lin, Zhongrui Wang, Yunning Li, Saumil Joshi, Rivu Midya, Can Li, Mingyi Rao, Mark Barnell, Qing Wu, J. Joshua Yang () and Qiangfei Xia ()
Additional contact information
Hao Jiang: University of Massachusetts
Daniel Belkin: University of Massachusetts
Sergey E. Savel’ev: Loughborough University
Siyan Lin: University of Massachusetts
Zhongrui Wang: University of Massachusetts
Yunning Li: University of Massachusetts
Saumil Joshi: University of Massachusetts
Rivu Midya: University of Massachusetts
Can Li: University of Massachusetts
Mingyi Rao: University of Massachusetts
Mark Barnell: Information Directorate
Qing Wu: Information Directorate
J. Joshua Yang: University of Massachusetts
Qiangfei Xia: University of Massachusetts

Nature Communications, 2017, vol. 8, issue 1, 1-9

Abstract: Abstract The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things.

Date: 2017
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DOI: 10.1038/s41467-017-00869-x

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