Abstract
Two-dimensional magnetic recording (TDMR) is a promising technique to achieve very high areal density up to 10 Tb/in2. Write/Read channel based on Voronoi model has been widely used to capture the effects of inter-track interference (ITI), inter-symbol interference (ISI) and irregular grains. Higher density relies on complicated 2-D signal processing algorithms. In this paper we improve the read process in terms of the reliability for read response. We develop a Neural Network Block Predictor (NNBP) to detect the recording data, then we investigate the most possible harmful transitions and patterns in TDMR.
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