EXTRACTION OF ELECTRICAL TEST PARAMETERS BY ARTIFICIAL NEURAL NETWORK

Authors

  • Joseph Ke Faculty of Engineering & Technology University of Multimedia
  • M V C Rao Faculty of Engineering & Technology University of Multimedia

DOI:

https://doi.org/10.23055/ijietap.2006.13.1.424

Keywords:

Artificial neural network, Back propagation, Electrical test, Process control monitoring, Wafer fabrication

Abstract

In semiconductor industry, cycle time of the wafer fabrication is very crucial and one of the contributing factors comes

from wafer testing. This paper presents the application of back propagation Artificial Neural Network (ANN) model

designed to infer electrical test parameters from the given list of parameters with the intention of reducing test time, to

enhance throughput, and to improve cycle time. It also investigates if the ANN based inference system can be established as

a robust method for parameter extraction to provide an accurate electrical value to minimize false measurement. It is shown

that the ANN model does quite an excellent job and the predicted values are in good agreement with the measured values

Author Biography

Joseph Ke, Faculty of Engineering & Technology University of Multimedia

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Published

2022-02-24

How to Cite

Ke, J., & Rao, M. V. C. (2022). EXTRACTION OF ELECTRICAL TEST PARAMETERS BY ARTIFICIAL NEURAL NETWORK. International Journal of Industrial Engineering: Theory, Applications and Practice, 13(1), 71–80. https://doi.org/10.23055/ijietap.2006.13.1.424

Issue

Section

Data Sciences and Computational Intelligence