Automatically Resolving Inter-Track Interference with Convolution Neural Network Detection Channel in TDMR

Convolution neural network (CNN) is used as the data detection channel for two-dimensional magnetic recording (TDMR). We demonstrate that by only feeding interfered multireader signals as well as the target binary data at supervised learning, it is possible for CNN to automatically resolve the intertrack interference (ITI) in a noisy environment without any physical modeling. It is worth mentioning that throughout the entire training/learning process, CNN never receives ITI-free waveforms as target nor is explicitly “instructed” to resolve ITI. We illustrate that the nonlinear detection capability plays the most important role for CNN to effectively “learn” the right correlation between multiple read channels under heavy noise. Therefore, ITI can be completely eliminated as long as the noise exhibits similar correlation, while the white electronic noise completely disturbs this correlation and “decorrelates” the channels, hence posing most serious limitations toward ITI mitigation capability for CNN.