TDMR With Machine Learning Data Detection Channel

In this article, we present a systematic study of using a machine learning (ML) data detection channel consisting of a convolutional neural network (CNN) for data recovery in a two-dimensional magnetic recording (TDMR) setting with two displaced readers. To mimic the actual head skew angle change over the entire disk platter, data recovery over a wide range of inter-track interference (ITI) has been investigated. During training, the CNN-based ML channel only "learns" to detect the main track data although the sampled input signals from both readers are taken as input. It is found that with reasonable training, the ML channel can almost completely eliminate the ITI-caused degradation of bit error rate (BER). Moreover, it is also found that the training processes are only needed at very few head skewing angles, adding to the viability of the possible practical implementation. We believe the understanding elucidated in this article could serve the basis for developing viable and robust ML-based data detection channels leading to significant areal density gain for TDMR technology.