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Leong, C.H.
Universitas Gadjah Mada
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Mathematics

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THERMAL PERFORMANCE PREDICTION OF PLASTICS BALL GRID ARRAY (PBGA) USING ARTIFICIAL NEURAL NETWORK Leong, C.H.; Azid, I.A.; Seetharamu, K.N.
ASEAN Journal on Science and Technology for Development Vol. 19 No. 1 (2002): ASEAN Journal on Science and Technology for Development (AJSTD)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.127 KB) | DOI: 10.29037/ajstd.327

Abstract

Artificial Neural Network (ANN) based on feed-forward backpropagation model is used  to predict junction temperature in PBG A package. The limited results obtained from FEM (using IDEAS software) are used to train the neural network. The effect of source power, substrate and mold compound thermal conductivity, die size, substrate thickness and air velocity on junction temperature and thermal resistance has been investigated using ANN.  The predicted junction temperature using ANN agrees closely with the prediction from FEM. ANN method takes a small fraction of the time and effort compared to that required by HEM for prediction.
Co-Authors Azid, I.A. Seetharamu, K.N.