Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards

[+] Author and Article Information
Tohru Suwa

Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030tsuwa@stevens.edu

Hamid Hadim

Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030ahadim@stevens.edu

J. Electron. Packag 129(1), 90-97 (Apr 19, 2006) (8 pages) doi:10.1115/1.2429715 History: Received December 12, 2005; Revised April 19, 2006

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 is developed for this study. Two genetic algorithms with different thermal prediction modules are used in a cascade in the optimization process. The first genetic algorithm uses 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 its capabilities, the present methodology is applied to a test case involving placement optimization of several heat generating electronics components on a PCB.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 1

Genetic algorithm optimization flowchart

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Figure 2

Thermal prediction model for step 2

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Figure 3

RBF network model

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Figure 4

Predicted junction-to-ambient thermal resistance using thermal prediction for Step 2 compared with CFD-thermal conjugate results for four BGA on a 100×100mm board

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Figure 5

Average temperature error for RBF network 2-a training

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Figure 6

Genetic algorithm objective function with different population size, average of 100 runs (Step 1)

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Figure 7

Placement optimization results from Step 1

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Figure 8

Step 1 results with different thermal and wiring density function weights

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Figure 9

Placement optimization result from Step 2

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Figure 10

Thermal performance functions for Steps 1 and 2



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