Comparing analytical, micromagnetic and statistical channel models at 4 Tcbpsi patterned media recording

• Main Authors: Cai, Kui, Elidrissi, Moulay Rachid, Eason, Kwaku, Chan, Kheong Sann, Goh, Wang Ling, Chua, M., Zhang, S. H., Qin, Z. L., Chai, K. S., Tan, K. P., Dong, Y., Victora, R. H.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/96022; http://hdl.handle.net/10220/11422
• Institution: Nanyang Technological University

Bit patterned media recording (BPMR) is a candidate technology proposed to extend the areal density growth capability of magnetic recording systems. In conventional granular magnetic recording (CGMR), bits of information are recorded onto a number of randomly distributed magnetic grains. The difficulty for the granular media approach is that due to the randomness of the grains, a certain minimum number of grains are required in order to maintain the media signal to noise ratio (SNR). This requires smaller grains as the bits are shrunk and we end up with either unstable, or unwriteable grains as per the well-known media-trilemma. In BPMR, instead of random positions, the "grains'' are ordered into a well-defined lattice of magnetic islands. Because of this ordering, the media SNR is no longer determined by the number of grains per bit as in CGMR, but is defined by the geometrical and magnetic distributions of the fabrication process of the media. Analytical models exist for BPMR, that flip grains in the media based on the switching field distribution (SFD) and the knowledge of the applied head field. On the other side, a statistical model for CGMR has been proposed that measures probabilities of grains flipping based on micromagnetic simulations, named the grain flipping probability (GFP) model. In this work, we adapt the GFP model for BPMR and perform a comparison between densities predicted via the analytical and GFP models, by processing the signals from these models with the appropriate detectors and LDPC (low density parity check) decoders.