NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions
Published
Author(s)
Temitayo Adeyeye, Sidra Gibeault, Daniel Lathrop, Matthew Daniels, Mark Stiles, Jabez McClelland, William Borders, Jason Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan
Abstract
Though exponential distributions are ubiquitous in statistical physics and related computational models, sampling them from device behavior is rarely done. The superparamagnetic tunnel junction (SMTJ), a key device in probabilistic computing, shows exponentially distributed temporal switching dynamics. To sample an exponential distribution with an SMTJ, we need to measure it in the time domain, which is challenging with traditional techniques that focus on sampling the instantaneous state of the device. In this work, we leverage temporal processing circuits, where information is encoded in the time of the resistive switching event, in order to experimentally extract the exponential distributions that are naturally available in the temporal switching properties of SMTJ devices. We developed a circuit that applies a current step to an SMTJ and measures the timing of the first switching event, confirming that these times are exponentially distributed. Temporal processing methods then allow us to digitally sample from these exponentially distributed probabilistic delay cells. We demonstrate how to use these circuits in a Metropolis-Hastings stepper and in a weighted random sampler, both of which are computationally intensive applications that benefit from the efficient generation of exponentially distributed random numbers.
Adeyeye, T.
, Gibeault, S.
, Lathrop, D.
, Daniels, M.
, Stiles, M.
, McClelland, J.
, Borders, W.
, Ryan, J.
, Talatchian, P.
, Ebels, U.
and Madhavan, A.
(2025),
Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.23.044047, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959219
(Accessed October 9, 2025)