Assembly yield prediction of plastically encapsulated packages with a large number of manufacturing variables by advanced approximate integration method. (November 2017)

• Record Type: Journal Article
• Authors: Wei, Hsiu-Ping; Han, Bongtae; Youn, Byeng D.; Shin, Hyuk; Kim, Ilho; Moon, Hojeong
• Is Part Of: Microelectronics and reliability. Volume 78(2017)
• Page Start: 319
• Page End: 330
• Publication Date: 2017-11
• Subjects: Approximate integration scheme -- Dimension reduction -- Probability density function -- Assembly yield -- Warpage -- Stacked die thin flat ball grid array; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2017.09.006
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: 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.