Neural Network Associative Memory Using Non-Linear Holographic Storage Media

Abstract

This thesis investigates an all optical holographic associative memory (HAM) which uses iron doped lithium niobate as the holographic recording and reading in thick, photorefractive material is performed. Initial experiments identify recording parameters which optimize the holographic diffraction efficiency of stored holograms. Increasing diffraction efficiency is reported during the object beam illumination of the hologram. This unexpected result is linked to the use of a thin, anisotropic, plastic diffuser in the object beam. The operational theory of a single iteration, multiple object HAM system is developed. Experiments are performed with the HAM, using a single stored object, to verify system operation. Distortion experiments are accomplished to qualitatively determine system performance when presented with a partial input. Complete object reconstruction was not achieved due to the single iteration HAM architecture. The HAM system achieved reconstruction of any one of the stored objects. However, once a single object was reconstructed, subsequent memory recall of other stored objects failed due to the dynamic holographic medium. The generation of one output image distorted the remaining holograms such that additional reconstructions were not possible. Lastly, a full resonant HAM operating in a closed loop optical cavity was investigated.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1989
Accession Number
ADA214340

Entities

People

  • Dwayne W. Frye

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Content Addressable Memory
  • Data Storage Systems
  • Diffraction
  • Elements
  • Energy Transfer
  • Lithium Niobates
  • Materials
  • Optical Correlators
  • Optical Phenomena
  • Optical Properties
  • Optics
  • Pattern Recognition
  • Phase Conjugation
  • Piezoceramics
  • Recognition
  • Refractive Index
  • Wave Mixing

Fields of Study

  • Engineering
  • Physics

Readers

  • Astronomy and Astrophysics.
  • Optical Physics and Photonics.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks