# Importance sampling for thermally induced switching and non-switching probabilities in spin-torque magnetic nanodevices

## TL;DR

This paper demonstrates how importance sampling can efficiently estimate very low switching and non-switching probabilities in spin-torque magnetic nanodevices, addressing the challenge of thermal fluctuations affecting memory reliability.

## Contribution

It introduces an importance sampling method tailored for accurately computing small error probabilities in magnetic nanodevices, improving upon naive Monte Carlo approaches.

## Key findings

- Efficient estimation of low error rates in magnetic memory devices.
- Variance reduction significantly improves computational efficiency.
- Applicable to both macrospin and coupled-spin systems.

## Abstract

Spin-transfer torque magnetoresistive random access memory is a potentially transformative technology in the non-volatile memory market. Its viability depends, in part, on one's ability to predictably induce or prevent switching; however, thermal fluctuations cause small but important errors in both the writing and reading processes. Computing these very small probabilities for magnetic nanodevices using naive Monte Carlo simulations is essentially impossible due to their slow statistical convergence, but variance reduction techniques can offer an effective way to improve their efficiency. Here, we provide an illustration of how importance sampling can be efficiently used to estimate low read and write soft error rates of macrospin and coupled-spin systems.