0
Journal of Electronic Packaging | Volume 129 | Issue 1 | Research Paper
RESEARCH PAPERS

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
Article
References
Figures
Tables
Citing Articles

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.
FIGURES IN THIS ARTICLE
<>
Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
+Genetic algorithm optimization flowchart
View Large  |  Save Figure  |  Download Slide (.ppt)
Genetic algorithm optimization flowchart
Figure 1

Genetic algorithm optimization flowchart

Grahic Jump Location
+Thermal prediction model for step 2
View Large  |  Save Figure  |  Download Slide (.ppt)
Thermal prediction model for step 2
Figure 2

Thermal prediction model for step 2

Grahic Jump Location
+RBF network model
View Large  |  Save Figure  |  Download Slide (.ppt)
RBF network model
Figure 3

RBF network model

Grahic Jump Location
+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
View Large  |  Save Figure  |  Download Slide (.ppt)
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
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
+Average temperature error for RBF network 2-a training
View Large  |  Save Figure  |  Download Slide (.ppt)
Average temperature error for RBF network 2-a training
Figure 5

Average temperature error for RBF network 2-a training

Grahic Jump Location
+Genetic algorithm objective function with different population size, average of 100 runs (Step 1)
View Large  |  Save Figure  |  Download Slide (.ppt)
Genetic algorithm objective function with different population size, average of 100 runs (Step 1)
Figure 6

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

Grahic Jump Location
+Placement optimization results from Step 1
View Large  |  Save Figure  |  Download Slide (.ppt)
Placement optimization results from Step 1
Figure 7

Placement optimization results from Step 1

Grahic Jump Location
+Step 1 results with different thermal and wiring density function weights
View Large  |  Save Figure  |  Download Slide (.ppt)
Step 1 results with different thermal and wiring density function weights
Figure 8

Step 1 results with different thermal and wiring density function weights

Grahic Jump Location
+Placement optimization result from Step 2
View Large  |  Save Figure  |  Download Slide (.ppt)
Placement optimization result from Step 2
Figure 9

Placement optimization result from Step 2

Grahic Jump Location
+Thermal performance functions for Steps 1 and 2
View Large  |  Save Figure  |  Download Slide (.ppt)
Thermal performance functions for Steps 1 and 2
Figure 10

Thermal performance functions for Steps 1 and 2

Tables

Errata

Discussions

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
Control and Operational Performance
Closed-Cycle Gas Turbines> Chapter 5
Outlook
Closed-Cycle Gas Turbines> Chapter 11
Tales of the JEDEC Knight
More Hot Air> Chapter 5
Components and Materials: The Sum of the Parts Is Sometimes Just a Big Hole
More Hot Air> Chapter 3
Optimization of Performance Specifications Based on Environmental Parameters
Inaugural US-EU-China Thermophysics Conference-Renewable Energy 2009 (UECTC 2009 Proceedings)
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