Phase-Coded Hologram Multiplexing for High Capacity Optical Data Storage,

Abstract

Reconfigurable volume holograms are important for a wide range of multiple data storage applications, including optical interconnection systems, image processing and neural network models. Therefore, there has been much previous work on multiplexing techniques to obtain a large number of stored images which can be recalled independently. But even the most promising of these multiplexing techniques, angular multiplexing using the selectivity of the Bragg-condition, revealed to be limited primarily because of cross correlation noise. Moreover, mechanically changing the incident angle of the reference beam requires a high reliability in the positioning and is therefore inherently slow. To over-come these problems, an intensity spatial light modulator can be used to define the various angular multiplexed incident directions. However, this solution is energy consuming. Out of these reasons, we present in this paper an alternative approach implementing a phase coding method of the reference beam. Phase encoding has been discussed for interconnecting vector arrays in thin hologram and, more recently in a different context, to perform array interconnections by correlation of a reference beam with a supplementary phase-coded input beams.

Document Details

Document Type
Technical Report
Publication Date
May 22, 1992
Accession Number
ADP006724

Entities

People

  • C. Denz
  • G. Pauliat
  • G. Roosen

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Coding
  • Computer Programming
  • Cross Correlation
  • Data Storage Systems
  • High Reliability
  • Holograms
  • Image Processing
  • Modulators
  • Multiplexing
  • Neural Networks
  • Optical Interconnects
  • Optical Modulators
  • Photorefractive Materials
  • Reliability

Fields of Study

  • Physics

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Optical Physics and Photonics.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference