Integrated Substrate and Thin Film Design Methods

Abstract

An artificial neural network cascade, containing 16 individual network modules and approximately 1,000 processing units, has produced an interactive database of nearly a quarter million potential binary and ternary chemical systems. While many of these hypothetical materials are anticipated to be thermodynamically stable, they are most likely kinetically inaccessible via typical bulk chemistry routes. However, since modem thin film technology allows a wide range of exotic compositions and stoichiometries via deposition, surface treatments, and nano-fabrication, it is anticipated that this newly acquired theoretical database will form a comprehensive road map to the formation of previously unattainable materials that offer significant technological advantages. Further, with the suite of available coating materials greatly expanded, thin film designers now have at their disposal the means to implement multilayer and composite thin film device designs that fulfill a much broader range of performance requirements and that are ideally matched to both underlying substrate and external working environment.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1999
Accession Number
ADA369790

Entities

People

  • Stephen L. Thaler

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Chemical Compounds
  • Chemical Properties
  • Chemistry
  • Electrical Conductivity
  • Fabrication
  • Materials
  • Materials Laboratories
  • Materials Processing
  • Materials Science
  • Materials Testing
  • Neural Networks
  • Refractive Index
  • Solid State Physics
  • Standards
  • Surface Finishing

Readers

  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design
  • Thin Film Deposition Science.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks