Hsiu-Ping Weia, Byeng Dong Younb, Bongate Hanc, Hyuk Shind, Ilho Kime, Hojeong Moone
a Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
b Department of Mechanical Aerospace Engineering, Seoul National University, Seoul, South Korea
c CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
d Package Development Team, Semiconductor R&D Center, Samsung Electronics, Republic of Korea
e Samsung, Seoul, South Korea
An advanced approximate integration scheme called eigenvector dimension reduction (EDR) method is implemented to predict the assembly yield of a plastically encapsulated package. A total of 12 manufacturing input variables are considered during the yield prediction, which is based on the JEDEC reflow flatness requirements. The method calculates the statistical moments of a system response (i.e., warpage) first through dimensional reduction and eigenvector sampling, and a probability density function (PDF) of random responses is constructed subsequently from the statistical moments by a probability estimation method. Only 25 modeling runs are needed to produce an accurate PDF for 12 input variables. The results prove that the EDR provides the numerical efficiency required for the tail-end probability prediction of manufacturing problems with a large number of input variables, while maintaining high accuracy.
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