CNN-Based Machine Learning Channel on TDMR Drive Data

In this article, we demonstrate a convolutional neural network (CNN)-based data detection channel on real data from a commercial hard disk drive (HDD) employed with two-dimensional magnetic recording (TDMR) dual-reader technology. The neural network has two sets of input neurons and is trained using 485 4-kB sectors from the drive. Data detection is performed using the trained network and a one-to-one comparison of bit-error-rate (BER) performance with the HDD channel is systematically conducted. The trained neural network demonstrates comparable or slightly better performance than the state-of-the-art HDD channel. In addition, comparing to detection with a single reader, CNN-machine learning (ML) channel autonomously enables similar to 6% recording density gain when the second reader is available, and there is no physical modeling needed to manually combine or "fuse" signals from the multiple readers. We believe that the understanding elucidated in this article is crucial for developing more capable, robust, and effective ML-based data detection channels w ith the potential to realize even greater areal density gain in TDMR systems and higher capacity HDD.