Utilization of multiple read heads for TMR prediction and correction in bit-patterned media recording

W Busyatras,P Kovintavewat,Y Nakamura,Y Okamoto,P Supnithi,C Warisarn,L M M Myint

doi:10.1063/1.4972801

Abstract

This paper proposes a utilization of multiple read heads to predict and correct a track mis-registration (TMR) in bit-patterned media recording (BPMR) based on the readback signals. We propose to use the signal energy ratio between the upper and lower tracks from multiple read heads to estimate the TMR level. Then, a pair of two-dimensional (2D) target and its corresponding 2D equalizer associated with the estimated TMR will be chosen to correct the TMR in the data detection process. Numerical results show that the proposed system can achieve a very high accuracy of TMR prediction, thus performing better than the conventional system, especially when TMR is severe.

Highlights

When the center of the read head is not aligned with that of the target track, it causes track mis-registration (TMR) or an off-track situation, which is one of major problems in bit-patterned media recording (BPMR), especially at high areal density (AD)
The TMR was predicted by the estimated signal-to-noise ratio (SNR) and the average energy of the readback signal.[3]
We propose to utilize three read heads to read the data on three adjacent tracks, and process them so as to predict and correct the TMR effect in the BPMR system

Summary

Introduction

When the center of the read head is not aligned with that of the target track, it causes track mis-registration (TMR) or an off-track situation, which is one of major problems in bit-patterned media recording (BPMR), especially at high areal density (AD). The servo mechanism can handle the TMR effect; it is difficult to control the read head when TMR occurs beyond its limit.[1,2]. The TMR was predicted by the estimated signal-to-noise ratio (SNR) and the average energy of the readback signal.[3] the accuracy of the TMR estimation from this method depends mainly on that of the SNR estimation. We found that these methods cannot accurately predict the TMR levels, especially when the system operates in a low TMR regime

Methods

Results

Conclusion