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Bit-Patterned Magnetic Recording: Nanoscale Magnetic Islands for Data Storage

  • Thomas R. Albrecht
  • Olav Hellwing
  • Ricardo Ruiz
  • Manfred E. Schabes
  • Bruce D. Terris
  • Xiao Z. Wu
Chapter

Abstract

Bit-patterned recording shows potential as a route to thermally stable data recording at densities greater than 1 Tbit/in2, provided that a number of challenging requirements can be met. Micromagnetic modeling of the write process shows that high write-field gradient (>350 Oe/nm) and tight tolerances on island fabrication and write synchronization (both in the range of ∼1 nm sigma) are required for addressability (the ability to write a given island without detrimentally affecting neighboring islands). Magnetically uniform islands are also required, with tight island switching-field distribution (5−10% of H k ). We show that magnetic multilayer films with perpendicular anisotropy (e.g., Co/Pd multilayers and laminated films of Co/Pd with other materials) are promising candidates for magnetic layer deposition onto pre-patterned substrates. A suitable strategy for patterned media fabrication begins with master pattern generation using electron beam lithography to create chemical contrast guiding patterns for self-assembly; this approach produces higher quality and higher density patterns than e-beam alone. Patterns are replicated over large volumes of disks by UV-cure nanoimprint lithography, followed by etching of the substrate or magnetic layer. Integration of bit-patterned media into a functional recording system requires write synchronization, in which the timing of current switching in the write head is synchronized with the passage of individual islands under the write head. Write synchronization may be implemented using a sector synchronization system, in which the write clock is frequency- and phase-locked to timing bursts read from the disk during periodic interruptions in the writing process.

Keywords

Block Copolymer Areal Density Magnetic Island Nanoimprint Lithography Switching Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Thomas R. Albrecht
    • 1
  • Olav Hellwing
    • 1
  • Ricardo Ruiz
    • 1
  • Manfred E. Schabes
    • 1
  • Bruce D. Terris
    • 1
  • Xiao Z. Wu
    • 1
  1. 1.Hitachi Global Storage TechnologiesSan Jose Research CenterSan JoseUSA

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