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
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

Genetic algorithm optimization flowchart

Grahic Jump Location
Figure 2

Thermal prediction model for step 2

Grahic Jump Location
Figure 3

RBF network model

Grahic Jump Location
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

Grahic Jump Location
Figure 5

Average temperature error for RBF network 2-a training

Grahic Jump Location
Figure 6

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

Grahic Jump Location
Figure 7

Placement optimization results from Step 1

Grahic Jump Location
Figure 8

Step 1 results with different thermal and wiring density function weights

Grahic Jump Location
Figure 9

Placement optimization result from Step 2

Grahic Jump Location
Figure 10

Thermal performance functions for Steps 1 and 2




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In