Improved Recording Performance With Writer Design and Joint Learning-Based Neural Network for Heat-Assisted Interlaced Magnetic Recording

Heat-assisted interlaced magnetic recording (HIMR) further increases the areal density with the interlaced track layout architecture compared to conventional heat-assisted magnetic recording. However, besides the 2-D inter-symbol interference (ISI) and thermal jitters, the transition curvatures due to the circular thermal profile cause severe erasures after write and obvious nonlinear distortions of readback signals. This deteriorates the recording performance severely. Especially the degrees of transition curvatures for the low temperature written track (LT) and high temperature written track (HT) are distinct, which makes the correction of transition curvature with only the writer design impossible. Correspondingly, a comprehensive method consisting of writer design and multitrack detection algorithm with joint learning-based neural network (JLNN) is proposed to mitigate the effects of transition curvature and 2-D ISI for both LT and HT of HIMR. First, a designed writer with splitting main poles produces the spatially nonuniform write field along the crosstrack direction to correct the transition curvature of LT. Then the multitrack detection with JLNN and improved Bahl–Cocke–Jelinek–Raviv (BCJR) detector (JLNN-IB) algorithm is studied to further mitigate the effects of residual transition curvatures, thermal jitters, and 2-D ISI for both HT and LT. Specifically, the readback signals of read head array and decoded log-likelihood ratio (LLR) outputted by the low-density parity check (LDPC) decoder are fed into JLNN to obtain the equalized signal and
$a$

priori
probability (AP) of three tracks. Then the equalized signal as well as AP of current track and corresponding soft bit estimations of adjacent tracks are embedded into the branch metrics of improved BCJR detector for data detection, and the LDPC decoder is cascaded for the error correction. The simulation indicates that at the recording density of 2.11 Tb/in
²
, the proposed comprehensive writer design and JLNN-IB algorithm provide 9.2 and 9.7 dB signal to noise ratio (SNR) gains compared to the conventional 2-D neural network equalizer and 2-D linear equalizer under the uniform write field and circular thermal profile, respectively.