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Programmable electrical coupling between stochastic magnetic tunnel junctions



Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Daniel Lathrop, Matthew Daniels, Mark Stiles, Jabez J. McClelland, William Borders, Jason Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan


Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of various probabilistic computing techniques. When augmented with electronic circuits, the random telegraph fluctuations of the resistance state of an SMTJ can be converted to stochastic digital signals, also known as probabilistic bits or $p$-bits. Though many proposals of scalable probabilistic computing methods have been recently proposed, practical implementations are limited by either minimal tunability or energy inefficient microprocessors-in-the-loop. In this work, we experimentally demonstrate the functionality of a simple scalable unit cell, namely a pair of $p$-bits with programmable electrical coupling. This tunable coupling is implemented with analog circuits that have a time constant of approximately 1\textmu}s, which is faster than the mean dwell times of the SMTJs over most of the operating range. Programmability enables flexibility, allowing both positive and negative couplings, as well as coupling devices with widely varying device properties. These tunable coupling circuits can achieve the whole range of correlations from $-1$ to $1$, for both devices with similar timescales, and devices whose time scales vary by an order of magnitude. This range of correlation allows such circuits to be used for processes similar to simulated annealing.
Physical Review Applied


magnetic tunnel junction, superparamagnetic, p-bit, telegraph noise


Gibeault, S. , Adeyeye, T. , Pocher, L. , Lathrop, D. , Daniels, M. , Stiles, M. , McClelland, J. , Borders, W. , Ryan, J. , Talatchian, P. , Ebels, U. and Madhavan, A. (2024), Programmable electrical coupling between stochastic magnetic tunnel junctions, Physical Review Applied, [online],, (Accessed May 26, 2024)


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Created March 29, 2024, Updated April 4, 2024