A multidisciplinary placement optimization methodology for heat generating electronics components is demonstrated at various levels of electronics packaging design. The proposed methodology is capable of handling a large number of conflicting multidisciplinary design requirements and complex trade-offs including thermal, mechanical, electrical, electromagnetic, cost among others, which are optimized simultaneously using a genetic algorithm. An effective thermal performance prediction methodology is developed to shorten the calculation time while retaining sufficient accuracy. For simpler thermal problems, a superposition method is used to predict the temperature distribution caused by arbitrarily placed multiple heat sources. For more complex problems (e.g. variable local heat transfer coefficient) artificial neural networks (ANNs) and the superposition method are combined for more efficient prediction of surface (case) and junction temperatures. The proposed methodology is designed to handle existing complex design trade-offs at the crucial early design stage. Capabilities of the present methodology are demonstrated by applying it to several standard benchmarks at the enclosure (PCB) and chip (logic block) levels.

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