• DocumentCode
    3252105
  • Title

    An approach to automatic test pattern generation using strictly digital neural networks

  • Author

    Arai, Masatoshi ; Nakagawa, Tohru ; Kitagawa, Hajime

  • Author_Institution
    Dept. of Inf. & Control Eng., Toyota Technol. Inst., Nagoya, Japan
  • Volume
    4
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    474
  • Abstract
    The authors present a parallel algorithm for finding a set of diagnostic patterns to test logic circuits using strictly digital neural networks (SDNNs). They use a new logic circuit called neural logic gate (NLG) to provide two logic functions, and obtain a preliminary set of test patterns. A circuit of the NLG is defined as intersecting sets of neurons with the k-out-of-n design rule, and has neither analog parameters nor stochastic operations. A problem is presented for test pattern generation using NLG to be solved by the SDNN system. The simulation results of automatic test pattern generation for a n-bit full-adder circuit up to 128 bit show that the order of computation is approximately O(n1.4) in parallel convergence, and O(n2.4) in sequential simulation. Compared with the original neural network, SDNN was able to find a set of test patterns more readily than the original neural network in large scale problems
  • Keywords
    convergence; logic testing; parallel algorithms; automatic test pattern generation; diagnostic patterns; digital neural networks; full-adder circuit; k-out-of-n design rule; logic circuit test generation; logic circuit testing; logic functions; neural logic gate; parallel algorithm; parallel convergence; sequential simulation; Automatic test pattern generation; Circuit testing; Computational modeling; Logic circuits; Logic functions; Logic gates; Logic testing; Neural networks; Neurons; Parallel algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.227299
  • Filename
    227299