Study of CNN-Based Data Detection in Dual-Layer Bit-Patterned Magnetic Recording Systems

Abstract

This paper introduces an innovative data detection system that utilizes convolutional neural networks (CNNs) for dual-layered bit-patterned magnetic systems. Using a mutual-information CNN architecture, the proposed system tackles the challenge of decoding overlapping readback signals from upper and lower layers. The sliding window detection schemes are implemented with input lengths of 6 (2×3) and 14 (2×7) bits, processing oversampled readback signals from a dataset of 1,000,000 bits. Simulation results conducted over a signal-to-noise ratio range of 10 to 24 dBs indicate that the CNN model with a larger input window significantly outperforms smaller input models and conventional partial response maximum likelihood detectors in terms of bit error rate. These findings illustrate the effectiveness of CNN-based detection in enhancing classification accuracy under high-noise conditions, paving the way for future ultra-high-density magnetic recording systems.