Deep Neural Network-based Detection and Partial Response Equalization for Multilayer Magnetic Recording

Abstract

The hard disk drive (HDD) industry stores data at areal densities close to the capacity limit of the onedimensional (1D) magnetic recording channel [1]. New technologies are emerging to increase density, including heat assisted magnetic recording (HAMR), microwave-assisted magnetic recording (MAMR), and two-dimensional magnetic recording (TDMR). TDMR employs 2D signal processing to achieve significant density gains, without changes to existing magnetic media. Recent encouraging studies [2] –[5] propose multilayer magnetic recording (MLMR): vertical stacking of an additional magnetic media layer to a TDMR system to achieve further density gains. Using a realistic grain flipping probability (GFP) model to generate waveforms [3], [4], we investigate the design of deep neural network (DNN) based methods for equalization and detection for MLMR.