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True random number generation using the spin crossover in LaCoO3.

Authors :
Woo, Kyung Seok
Zhang, Alan
Arabelo, Allison
Brown, Timothy D.
Park, Minseong
Talin, A. Alec
Fuller, Elliot J.
Bisht, Ravindra Singh
Qian, Xiaofeng
Arroyave, Raymundo
Ramanathan, Shriram
Thomas, Luke
Williams, R. Stanley
Kumar, Suhas
Source :
Nature Communications; 5/31/2024, Vol. 15 Issue 1, p1-9, 9p
Publication Year :
2024

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 LaCoO<subscript>3</subscript> that is electrically biased within its spin crossover regime. The LaCoO<subscript>3</subscript> 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. Probabilistic computing demands low power and high quality random number generation. Woo et al. demonstrate the use of a spin crossover in LaCoO3 to generate random numbers that outperform software-generated random numbers in probabilistic computing. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
177596841
Full Text :
https://doi.org/10.1038/s41467-024-49149-5