Assembly yield prediction of plastically encapsulated packages with a large number of manufacturing variables by advanced approximate integration method

Wei, Hsiu-Ping; Han, Bongtae; Youn, Byeng D.; Shin, Hyuk Soo; Kim, Il-Ho; Moon, Hojeong

doi:10.1016/j.microrel.2017.09.006

Cited by 9 publications

(3 citation statements)

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“…The nominal dimensions of the TFBGA package are adopted from the design in Refs. [10,31]. A finite element (FE) model was constructed for warpage prediction.…”

Section: Implementation For Semiconductor Packaging Applicationmentioning

confidence: 99%

“…The nominal dimensions of the TFBGA package are adopted from the design in Refs. [10,31]. The TFBGA package was subjected to the EMC molding process at 175 °C, which was used as a stress-free temperature.…”

Section: Description Of Tfbga Packagementioning

confidence: 99%

“…As the number of input variables increases, the computational cost may become prohibitively high due to an extremely large number of sample points required (this is the well-known "curse of dimensionality"). This situation will be exacerbated when the modeling requires a computationally expensive analysis such as time-dependent and nonlinear analysis that is routinely encountered in complex semiconductor packaging architectures [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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A 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.

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“…The nominal dimensions of the TFBGA package are adopted from the design in Refs. [10,31]. A finite element (FE) model was constructed for warpage prediction.…”

Section: Implementation For Semiconductor Packaging Applicationmentioning

confidence: 99%

Section: Description Of Tfbga Packagementioning

confidence: 99%