• DocumentCode
    1716401
  • Title

    Artificial neural networks for reverse engineering bipolar transistors

  • Author

    Ferguson, Ryan ; Roulston, David J.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada
  • Volume
    2
  • fYear
    1997
  • Firstpage
    459
  • Abstract
    In this paper we report on progress made in developing an artificial neural network which can reverse engineer the physical descriptions of bipolar transistors from a complete set of electrical data. The neural network tool REED (Rapid Engineering of Electron Devices) is used to perform a series of SPICE to BIPOLE3 mappings
  • Keywords
    SPICE; bipolar transistors; learning (artificial intelligence); neural nets; reverse engineering; semiconductor device models; semiconductor process modelling; BIPOLE3; REED; SPICE; artificial neural network; bipolar transistors; electrical data; impurity profile approximation; neural network tool; rapid engineering of electron devices; reverse engineering; Artificial neural networks; Backpropagation; Bipolar transistors; Data engineering; Electrons; Feedforward neural networks; Feedforward systems; Neural networks; Reverse engineering; SPICE;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Microelectronics, 1997. Proceedings., 1997 21st International Conference on
  • Conference_Location
    Nis
  • Print_ISBN
    0-7803-3664-X
  • Type

    conf

  • DOI
    10.1109/ICMEL.1997.632868
  • Filename
    632868
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1716401