Signal Detection Using Extrinsic Information From Neural Networks for Bit-Patterned Media Recording

Abstract

To meet the demand for storing large amounts of data, there has been a stronger focus on increasing the recording density of a hard disk drive (HDD). While conventional HDD suffers from thermal effect and superparamagnetic limit, bit-patterned media recording (BPMR) has the potential to resolve these problems and is capable of increasing areal density (AD) beyond 1 terabit per square inch. To increase the AD of BPMR, the distance between islands in both the down- and cross-track directions must be reduced. Consequently, the decreased bit period and track pitch induce more intersymbol interference (ISI) and intertrack interference (ITI), which degrade the bit error rate (BER) performance. In this study, extrinsic information obtained from a multilayer perceptron (MLP) equalizer is used as a priori information in a partial response maximum-likelihood (PRML) detector to improve the detection performance of BPMR. The proposed detection scheme shows better performance compared to both the MLP equalizer and conventional PRML alone. In addition, it was found that the proposed detector provides improved performance when track misregistration (TMR) occurs.