Implementation and Performance Evaluation of a Bivariate Cut-HDMR Metamodel for Semiconductor Packaging Design Problems with a Large Number of Input Variables

dc.contributor.authorYang, Yu-Hsiang
dc.contributor.authorWei, Hsiu-Ping
dc.contributor.authorHan, Bongtae
dc.contributor.authorHu, Chao
dc.date.accessioned2023-10-30T18:03:21Z
dc.date.available2023-10-30T18:03:21Z
dc.date.issued2021-08-17
dc.description.abstractA metamodeling technique based on Bivariate Cut High Dimensional Model Representation (Bivariate Cut HDMR) is implemented for a semiconductor packaging design problem with 10 design variables. Bivariate Cut-HDMR constructs a metamodel by considering only up to second-order interactions. The implementation uses three uniformly distributed sample points (s = 3) with quadratic spline interpolation to construct the component functions of Bivariate Cut-HDMR, which can be used to make a direct comparison with a metamodel based on Central Composite Design (CCD). The performance of Bivariate Cut-HDMR is evaluated by two well-known error metrics: R-squared and Relative Average Absolute Error (RAAE). The results are compared with the performance of CCD. Bivariate Cut HDMR does not compromise the accuracy compared to CCD, although the former uses only one-fifth of sample points (201 sample points) required by the latter (1045 sample points). The sampling schemes and the predictions of cut-planes and boundary-planes are discussed to explain possible reasons for the outstanding performance of Bivariate Cut HDMR.
dc.description.urihttps://doi.org/10.3390/ma14164619
dc.identifierhttps://doi.org/10.13016/dspace/xkta-hgiu
dc.identifier.citationYang, Y.-H.; Wei, H.-P.; Han, B.; Hu, C. Implementation and Performance Evaluation of a Bivariate Cut-HDMR Metamodel for Semiconductor Packaging Design Problems with a Large Number of Input Variables. Materials 2021, 14, 4619.
dc.identifier.urihttp://hdl.handle.net/1903/31197
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtMechanical Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectbivariate cut-HDMR
dc.subjectsemiconductor packaging
dc.subjectcentral composite design
dc.subjectR-squared
dc.subjectrelative average absolute error
dc.titleImplementation and Performance Evaluation of a Bivariate Cut-HDMR Metamodel for Semiconductor Packaging Design Problems with a Large Number of Input Variables
dc.typeArticle
local.equitableAccessSubmissionNo

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