Multitrack Detection With 2-D Iterative Soft Estimate Aided Neural Network Equalizer for Heat-Assisted Interlaced Magnetic Recording

Abstract

Heat-assisted interlaced magnetic recording (HIMR) with interlaced track layout architecture enables the further increased areal density compared to the conventional heat-assisted magnetic recording. However, the severe transition curvatures of low temperature written tracks cause noticeable nonlinear distortions of readback signal, and the 2-D intersymbol interference (ISI), media noise, and thermal jitter also bring the challenges for data recovery. Correspondingly, a multitrack detection scheme with 2-D iterative soft estimate aided neural network equalizer (2D-ISA NNE) is proposed for the HIMR system, which iteratively feeds back the soft estimate of reliable sidetracks’ information (e.g., high temperature written tracks) during the neural network equalization (NNE) of middle track (e.g., low temperature written track). Here, the Bahl–Cocke–Jelinek–Raviv (BCJR) detector and low-density parity-check (LDPC) decoder are utilized for the following data detection and error corrections. Then, the similar ISA NNE is implemented to recover the sidetracks’ data. It is found that the proposed 2D-ISA NNE algorithm mitigates the 2-D ISI and nonlinear distortion more effectively compared to the conventional 2-D linear equalizer (LE) and neural network equalizer (NNE). For HIMR at the channel bit density of 3.51 Tb/in2 and overlapping ratio of 0.46, the proposed 2D-ISA NNE algorithm significantly decreases the bit error rate gap between the low temperature and high temperature written tracks. For the low temperature written track, it provides 3.1 and 4 dB signal-to-noise ratio (SNR) gains compared to the conventional 2-D NNE and 2-D LE, respectively.