Open AccessTHERMAL PERFORMANCE PREDICTION OF PLASTICS BALL GRID ARRAY (PBGA) USING ARTIFICIAL NEURAL NETWORK
Author(s) -
C. H. Leong,
Ishak Abdul Azid,
K. N. Seetharamu
Publication year - 2017
Publication title -
asean journal on science and technology for development/asean journal on science and technology for development
Language(s) - English
Resource type - Journals
eISSN - 2224-9028
pISSN - 0217-5460
DOI - 10.29037/ajstd.327
Subject(s) - ball grid array , artificial neural network , materials science , thermal conductivity , backpropagation , thermal resistance , junction temperature , thermal grease , thermal , die (integrated circuit) , composite material , electronic engineering , biological system , computer science , engineering , artificial intelligence , nanotechnology , thermodynamics , soldering , physics , biology
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.