Entropy-assisted nanosecond stochastic operation in perpendicular superparamagnetic tunnel junctions

Published

July 2, 2025

Author(s)

Lucile Soumah, Louise Desplat, Nhat-Tan PHAN, Ahmed SIDI EL VALLI, Advait Madhavan, Florian DISDIER, Auffret Stéphane, Ricardo Sousa, Ursula Ebels, Mark Stiles, Philippe Talatchian

Abstract

We demonstrate a good agreement between mean dwell times measured in 50nm diameter, perpendicularly magnetized superparamagnetic tunnel junctions (SMTJ), and theoretical predictions based on Langer's theory. Due to a large entropic contribution, the theory yields Arrhenius prefactors in the femtosecond range for the measured junctions, in stark contrast to the typically assumed value of 1 ns. Thanks to the low prefactors, and fine-tuning of the perpendicular magnetic anisotropy, we report measured mean dwell times as low as 2.7 ns under an in-plane applied field at negligible bias voltage. Under a perpendicular applied field, we predict a Meyer-Neldel compensation phenomenon, whereby the prefactor scales like an exponential of the activation energy, in line with the exponential dependence of the measured dwell time on the field. We further predict the occurrence of (sub)nanosecond dwell times as a function of effective anisotropy and junction diameter at zero bias voltage. These findings pave the way towards the development of ultrafast, low-power, unconventional computing schemes operating by leveraging thermal noise in perpendicular SMTJs, which can be scaled down below 20 nm.

Citation

Physical Review Applied

Volume

24

Pub Type

Journals

Download Paper

https://doi.org/10.1103/v53b-mz5b

Local Download

Keywords

Magnetic Tunnel Junction, MTJ, Superparamagnetism, Stochastic Computing, Probabilistic Computing

Citation

Soumah, L. , Desplat, L. , PHAN, N. , SIDI EL VALLI, A. , Madhavan, A. , DISDIER, F. , Stéphane, A. , Sousa, R. , Ebels, U. , Stiles, M. and Talatchian, P. (2025), Entropy-assisted nanosecond stochastic operation in perpendicular superparamagnetic tunnel junctions, Physical Review Applied, [online], https://doi.org/10.1103/v53b-mz5b, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959930 (Accessed October 9, 2025)