  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.
