Statistical analysis of read-back signals in magnetic recording on granular media

TL;DR

This paper introduces a probabilistic method for simulating magnetic recording on granular media, significantly reducing computational costs while accurately estimating the mean read-back signal and noise, especially in heat-assisted recording scenarios.

Contribution

The authors propose a probability mapping approach that avoids explicit magnetization state simulation, enabling efficient statistical analysis of magnetic recording signals.

Findings

Improved accuracy in signal-to-noise ratio estimation.

Reduced computational effort compared to traditional methods.

Effective in heat-assisted magnetic recording simulations.

Abstract

The comprehensive simulation of magnetic recording, including the write and read-back process, on granular media becomes computationally expensive if the magnetization dynamics of each grain are explicitly computed. In addition, in heat-assisted magnetic recording, the writing of a single track becomes a random process since the temperature must be considered and thermal noise is involved. Further, varying grain structures of various granular media must also be taken into account to obtain correct statistics for the final read-back signal. Hence, it requires many repetitions of the write process to investigate the mean signal as well as the noise. This work presents a method that improves the statistical evaluation of the whole recording process. The idea is to avoid writing the magnetization to one of its binary states. Instead, we assign each grain its probability of occupying one of its stable states, which can be calculated in advance in terms of a switching probability phase diagram. In the read-back process, we combine the probabilities to calculate a mean signal and its variance. Afterwards, repetitions on different media lead to the final read-back signal. Using a recording example, we show that the statistical behavior of the evaluated signal-to-noise ratio can be significantly improved by applying this probability mapping method, while the computational effort remains low.