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.
Joonghvun Back, "Design Guideline for Thermal performance of Microelectronic Packages". ISPS'97 Proceedings, San Diego, California, December 2-5, pp. 226-231: 1997.
Habib Abul Mustain, "Thermo-Mechanical Analysis of PBGA Electronic". Master's thesis, School of Mechanical Engineering, University of Science, Malaysia. 1997.
S. Vikrum, "Design of Thermal Expert System Using Neural Network Based Thermal Calculators". The International JournaI of Microcircuits and Electronic Packaging, Vol. 2, No. 4, pp. 334-381. Fourth Quarter, 1998.
User's Guide. MATLAB version 5.3, Neural Network Toolbox, The Math Work Inc .
Leong Hoe, "Application of Artificial Neural Network in Electronic Packaging”. Final year project. School of Mechanical Engineering, University of Science, Malaysia, March 2001.