True random number generation using the spin crossover in LaCoO3

Kyung Seok Woo, Alan Zhang, Allison Arabelo, Timothy D. Brown, Minseong Park, A. Alec Talin, Elliot J. Fuller, Ravindra Singh Bisht, Xiaofeng Qian, Raymundo Arroyave, Shriram Ramanathan, Luke Thomas, R. Stanley Williams () and Suhas Kumar ()
Additional contact information
Kyung Seok Woo: Sandia National Laboratories
Alan Zhang: Sandia National Laboratories
Allison Arabelo: Texas A&M University
Timothy D. Brown: Sandia National Laboratories
Minseong Park: Sandia National Laboratories
A. Alec Talin: Sandia National Laboratories
Elliot J. Fuller: Sandia National Laboratories
Ravindra Singh Bisht: The State University of New Jersey
Xiaofeng Qian: Texas A&M University
Raymundo Arroyave: Texas A&M University
Shriram Ramanathan: The State University of New Jersey
Luke Thomas: Applied Materials Inc.
R. Stanley Williams: Sandia National Laboratories
Suhas Kumar: Sandia National Laboratories

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

Date: 2024
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DOI: 10.1038/s41467-024-49149-5

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