Determination of thermal compact model via evolutionary genetic optimization method

Arunasalam, Parthiban; Seetharamu, K.N.; Azid, Ishak Abdul

doi:10.1109/tcapt.2005.848538

Cited by 19 publications

(6 citation statements)

References 19 publications

(30 reference statements)

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“…The feasibility of applying a stochastic search algorithms was studied to derive a steady-state CTM by several authors [7][8][9], which demonstrate the high capabilities of Genetic Algorithms for optimization.…”

Section: Concurrent Model Reduction Approachesmentioning

confidence: 99%

Multi-port dynamic compact thermal models of dual-chip package using model order reduction and metaheuristic optimization

Rogié

Codecasa

Monier-Vinard

et al. 2018

Microelectronics Reliability

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Delphi-like boundary condition independent (BCI) compact thermal models (CTMs) are the standard for modelling single die packages. However their extraction, particularly in the transient case, will be time consuming due to complex numerical simulations for a large number of external conditions. Lately, new approaches to extract a BCI dynamical CTM (DCTM), based on model order reduction (MOR) were developed. Despite the numerous advantages of this recent method, the lack of numerical tools to integrate reduced-order models (ROM) makes it difficult to use at board level. In this study, a novel process flow for extracting Delphi-inspired BCI DCTMs is proposed. Thus a detailed three-dimensional model is replaced by a BCI-ROM model using FANTASTIC matrix reduction code to generate the data used in the creation of a Delphi-style BCI DCTM. That hybrid reduction method has been applied, at first on a single-chip package (QFN16) then on a dual-chip package (DFN12). Their derived CTM and DCTM have been compared in term of accuracy and creation time using, or not, MOR reduction technique. The results show that for a similar accuracy, the integration of MOR technique allows minimizing the time-consuming numerical simulations and consequently reduce the thermal network creation time by 80%.

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Section: Concurrent Model Reduction Approachesmentioning

confidence: 99%

Multi-port dynamic compact thermal models of dual-chip package using model order reduction and metaheuristic optimization

Rogié

Codecasa

Monier-Vinard

et al. 2018

Microelectronics Reliability

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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%

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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“…GA has been widely used for optimization purposes in microelectronic problems as shown in previous works by Man et al . [ 25 ], Arunasalam et al [ 26 ] and Jeevan et al [ 27 ], while a more detailed description of GA can be found texts written by Goldberg [ 24 ], Mitchell [ 28 ] and Houck et al [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System

Lee

Azid

2009

Sensors

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In this study, a hybridized neuro-genetic optimization methodology realized by embedding numerical simulations trained artificial neural networks (ANN) into a genetic algorithm (GA) is used to optimize the flow rectification efficiency of the diffuser element for a valveless diaphragm micropump application. A higher efficiency ratio of the diffuser element consequently yields a higher flow rate for the micropump. For that purpose, optimization of the diffuser element is essential to determine the maximum pumping rate that the micropump is able to generate. Numerical simulations are initially carried out using CoventorWare® to analyze the effects of varying parameters such as diffuser angle, Reynolds number and aspect ratio on the volumetric flow rate of the micropump. A limited range of simulation results will then be used to train the neural network via back-propagation algorithm and optimization process commence subsequently by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to maximize the efficiency ratio of the diffuser element for the range of parameters investigated. The optimized efficiency ratio obtained from the neuro-genetic optimization is 1.38, which is higher than any of the maximum efficiency ratio attained from the overall parametric studies, establishing the superiority of the optimization method.

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Determination of thermal compact model via evolutionary genetic optimization method

Cited by 19 publications

References 19 publications

Multi-port dynamic compact thermal models of dual-chip package using model order reduction and metaheuristic optimization

Multi-port dynamic compact thermal models of dual-chip package using model order reduction and metaheuristic optimization

Boundary-Condition-Independent Compact Models

Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System

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