Deep Neural Network-based Detection and Partial Response Equalization for Multilayer Magnetic Recording
Ahmed Aboutaleb, Amirhossein Sayyafan, Benjamin J. Belzer, Krishnamoorthy Sivakumar, Simon John Greaves, Kheong Sann Chan, Roger W. Wood · 2020
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.