2013
DOI: 10.1109/tcpmt.2013.2271244
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Study of Response Surface Methodology in Thermal Optimization Design of Multichip Modules

Abstract: A 3-D model of multichip module (MCM) is built with ANSYS and the temperature field distribution is studied. A regression equation describing the relationship of structure parameters and material properties with the maximum chip junction temperature of MCM is made, which integrates the response surface methodology and ANSYS. Quantitative analysis of the effect of four design parameters on the maximum chip junction temperature of MCM is studied. The four design parameters are the thickness of the substrate, the… Show more

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Cited by 18 publications
(6 citation statements)
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References 15 publications
(11 reference statements)
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“…The size of the training region X s is defined by a vector τ ∈ ℜ n containing the maximum relative deviation for each design variable with respect to x (0) . To train the surrogate model, we use L learning base points within X s , denoted as x (1) , x (2) , …, x (L) . To measure the generalization error of the surrogate model, we use T testing base points.…”
Section: Training the Polynomial Surrogate Modelsmentioning
confidence: 99%
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“…The size of the training region X s is defined by a vector τ ∈ ℜ n containing the maximum relative deviation for each design variable with respect to x (0) . To train the surrogate model, we use L learning base points within X s , denoted as x (1) , x (2) , …, x (L) . To measure the generalization error of the surrogate model, we use T testing base points.…”
Section: Training the Polynomial Surrogate Modelsmentioning
confidence: 99%
“…For the third-order surrogate model, we want to match the fine model response and the corresponding surrogate model response at the j-th learning base point and k-th frequency, R sk (0) (x (j) ) + w k (1)T q (1) (Δx (j) ) + w k (2)T q (2) (Δx (j) ) + w k (3)T q (3) (Δx (j) ) = R fk (x (j) ) (9) Applying (9) for j = 1, …, L, the surrogate model weighting factors can be calculated by solving for W (3) the following system of linear equations…”
Section: B Calculating All Weighting Factors Simultaneously For Eachmentioning
confidence: 99%
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“…In the surrogate modelling (SM) approach, the response surface technique is combined with design of experiment (DoE) sampling to generate the surrogate model representing the physical process being modelled. SM can be categorised into two distinct groups namely interpolation SM such as the Kriging model [1] and regression SM such as the quadratic polynomial model [2].…”
Section: Reduced Order Modellingmentioning
confidence: 99%
“…Several new techniques have yielded promising results toward establishing peak temperature for GaN devices in combination with detailed modeling and infrared (IR) imaging (Green et al, 2008;Raj & Bindra, August 2013;Salem, Ibitayo, & Geil, 2007;Sommet, Mouginot, Quere, Ouarch, & Camiade, 2012;J. Zhang & Zhang, 2013).…”
Section: Electro-thermal Analysismentioning
confidence: 99%