Signal Processing for Two-Dimensional Magnetic Recording

With magnetic storage devices already achieving storage densities of up to 400 Gigabits per square inch (Gb/in2), the state of the art is rapidly approaching theoretical limits (dictated by thermal stability concerns). Hence, there is an eort in the industry to develop alternative magnetic storage t...

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Bibliographic Details
Main Author: Krishnan, Anantha Raman
Other Authors: Vasic, Bane
Language:en
Published: The University of Arizona. 2011
Subjects:
Online Access:http://hdl.handle.net/10150/202984
Description
Summary:With magnetic storage devices already achieving storage densities of up to 400 Gigabits per square inch (Gb/in2), the state of the art is rapidly approaching theoretical limits (dictated by thermal stability concerns). Hence, there is an eort in the industry to develop alternative magnetic storage technologies. Two-dimensional magnetic recording (TDMR) is one such candidate technology. In contrast to other technologies(e.g. heat-assisted magnetic recording [1], bit-patterned media [2]) which rely on signicant changes being made to the recording medium, TDMR relies on the use of traditional recording media, while relying on signal processing to make improvements in the recording density. Though advantageous due to the fact that no drastic re-engineering of media is required, there are signicant challenges that need to be addressed in order to make TDMR a viable candidate for next-generation recordingsystems.The main challenges involved in TDMR arise due to (i) the small bit-area, along with an aggressive write/read process, which leads to a large amount of noise, and (ii) the two-dimensional nature of the recording process { so far not encountered in today's systems. Thus, a gamut of 2D signal processing algorithms need be developed for the compensation of errors occurring due to the aggressive write/read processes. In this dissertation, we present some of the work done with regard to the signal processing tasks involved in TDMR. In particular, we describe our work on (i) channel modelling, (ii) detection strategies, and (iii) error-correction coding strategies targetted at TDMR.