Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96022
Title: Comparing analytical, micromagnetic and statistical channel models at 4 Tcbpsi patterned media recording
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.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Chua, M., Elidrissi, M. R., Eason, K., Zhang, S. H., Qin, Z. L., Chai, K. S., et al. (2012). Comparing analytical, micromagnetic and statistical channel models at 4 Tcbpsi patterned media recording. IEEE Transactions on Magnetics, 48(5), 1826-1832.
Series/Report no.: IEEE transactions on magnetics
Abstract: 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.
URI: https://hdl.handle.net/10356/96022
http://hdl.handle.net/10220/11422
DOI: 10.1109/TMAG.2011.2169654
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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