Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards Using Artificial Neural Networks

A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) is presented. The methodology includes thermal, electrical and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location which are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology which is based on a combination of a superposition method and artificial neural networks (ANNs) is developed for this study. Two genetic algorithms with different thermal prediction methods are used in a cascade in the optimization process. The first genetic algorithm is based on simplified thermal network modeling and it is mainly aimed at finding component locations that avoid any overlap. Compact thermal models are used in the second genetic algorithm leading to more accurate thermal prediction which improves the placement optimization obtained using the first algorithm. Using this optimization methodology, large calculation time reduction is achieved without losing accuracy. To demonstrate the capabilities of the present methodology, a test case involving component placement on a PCB is presented.