Compact modeling approach using genetic algorithms for accurate thermal simulation

Koyamada, Koji; Yamada, Yasuharu; Nishio, Toshihiko; Kotera, Hidetoshi

doi:10.1002/1523-1496(200101)30:1<28::aid-htj4>3.0.co;2-3

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Readers who are interested in the application of genetic algorithms and neural networks to result in compact thermal models may consult Koyamada et al, 17 Aranyosi et al, 18 Clarksean et al, 19 Arunasalam et al 20 and Mallet et al 21 See also the next subsection. …”

Section: Other Approaches Discussed In Literaturementioning

confidence: 99%

“…In general, the 5R star (with optimized area ratios) performs much better than the 3R star. Furthermore, the 5R star scores much worse than the 7R non-star model (a factor [10][11][12][13][14][15][16][17][18][19][20], even for the subsets. Despite this, the data show that the 5R star gives acceptable maximum errors for the junction temperature in the order of 10%, provided we limit the field of application to, e.g., free or forced convection only.…”

Section: "38" and "99" Sets Reduced Sets And Subsets Dotcomp Tqfpmentioning

confidence: 99%

See 1 more Smart Citation

Boundary-Condition-Independent Compact Models

Lasance

Sabry

Bar‐Cohen

2014

Encyclopedia of Thermal Packaging

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This chapter gives a complete survey of resistive network type BCI compact thermal models. Several benchmarks representing different package families are discussed, how exactly BCI models are generated via optimization starting from detailed models, including how the topology is selected, how the cost function is defined, what the level of accuracy is, what the differences are between various optimization techniques, and especially to which accuracy the compact models should be compared. The Chapter is closed with a discussion of papers from other than DELPHI sources.

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Section: Other Approaches Discussed In Literaturementioning

confidence: 99%

Section: "38" and "99" Sets Reduced Sets And Subsets Dotcomp Tqfpmentioning

confidence: 99%

Boundary-Condition-Independent Compact Models

Lasance

Sabry

Bar‐Cohen

2014

Encyclopedia of Thermal Packaging

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“…Koyamada et al [12] have used GA to identify the variables such as the boundary conditions and thermal conductivities that are most important in high accuracy calculations and applied to a keyboard model. The parameters to be optimized in their studies are the thermal conductivities and contact thermal resistances at boundary.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters to be optimized in their studies are the thermal conductivities and contact thermal resistances at boundary. Koyamada et al [12] regard the identification of parameters, equivalent thermal conductivities and contact thermal resistance values, as an optimization problem that involves minimizing the difference between the predicted and measured results. They have used GA for their optimization.…”

Section: Introductionmentioning

confidence: 99%

Determination of thermal compact model via evolutionary genetic optimization method

Arunasalam

Seetharamu

Azid

2005

IEEE Trans. Comp. Packag. Technol.

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Genetic Algorithms (GA) are adaptive search algorithms based on the theory of natural selection and survival of the fittest. In this study, GA was used to derive a thermal compact model of a micro lead frame package. The GA derived model was then used to compute the junction temperature ( ) of the package for various boundary conditions. The results obtained were checked against simulation results of a detailed thermal model and were found to be within 1 5% of error. Computational time taken by the detailed finite element model required approximately 4 min whereas the GA derived model took less than 35 s to generate the of the package. Further, the study shows the feasibility and potential of applying GA as a powerful tool for optimization.Index Terms-Genetic algorithms (GA), micro lead frame package and thermal compact model/compact thermal model.

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“…The solution to this problem is fust to create a reduced model of the detailed model by a technique called Design Optimisation [23,24]. Subsequent fitting is very well feasible and has been demonstrated in other technology fields.…”

Section: On the Experimental Calibration Of Detailed Modelsmentioning

confidence: 99%

Recent progress in compact thermal models

Lasance

Ninteenth Annual IEEE Semiconductor Thermal Measurement and Management Symposium, 2003.

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The paper presents a number of recent developments in compact thermal modelling. A substantial part is devoted to a discussion on the accuracy of Boundary-Conditionlndependent (BCI) compact thermal models (CTMs) in practical situations. It bas been repeatedly shown that high accuracy can be obtained when comparing CTMs to detailed model results. However, these results are usually generated using certain assumptions. The validity of these assumptions is the main subject of this paper. An important conclusion is that the 'traditionally' generated CTMs perform very well in 'real-life' environments. Other recent developments that are shortly reviewed are: progress in dynamic compact modelling, the possibility of parameterisation, experimental calibration, and standardisation of CRvls. On the accuracy of compact thermal modelsSince the publication of the fust paper on BCI models by the DELPHI consortium [I], the concept has attracted wide attention because of its potentially high accuracy. These models enable the system level designer to predict tbe temperature of critical components with the accuracy required for reliability and performance prediction, without knowing all the details of the component in question. An overview of the principles of CTM generation can be found in Appendix A. The accuracy of Boundary-Condition-Independent (BCI) compact thermal models is still a matter of dispute because it is often not clear to what accuracy it should be compared.

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Compact modeling approach using genetic algorithms for accurate thermal simulation

Cited by 5 publications

References 4 publications

Boundary-Condition-Independent Compact Models

Boundary-Condition-Independent Compact Models

Determination of thermal compact model via evolutionary genetic optimization method

Recent progress in compact thermal models

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