Effect of substrate warpage on flip chip BGA thermal stress simulation

Meng, Chai Chee; Stoeckl, S.; Pape, H.; Yee, Foo Mun; Min, Tan Ai

doi:10.1109/eptc.2010.5702691

Cited by 5 publications

(1 citation statement)

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“…As consumers call for high performance mobile devices like laptops in thinner form factors, there is a push in the industry to make the electronic packages that go into them thinner. These thin packages need to be designed carefully as high warpage in these parts can lead to assembly and reliability issues like bump bridging and solder extrusion [1], [2]. Finite element analysis (FEA) would be a good tool for warpage prediction if accurate material properties were available.…”

Section: Introductionmentioning

confidence: 99%

Learning Localized Spatial Material Properties of Substrates in Ultra-Thin Packages Using Markov Chain Monte Carlo and Finite Element Analysis

2020

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Thinned silicon dies and thin substrates using thin core and coreless structures have enabled thin packages. For robust manufacturing and reliability of these parts, solving the warpage problem is key. While current finite element methodologies can provide some insights at the design stage, these simulations are only as accurate as the inputs such as the material properties and the stress-free temperatures. Electronic substrates are especially challenging to characterize and model as they are laminates consisting of a core with layers of resin and metal lines on either side. In this work, a hybrid approach using Markov Chain Monte Carlo (MCMC) and Finite Element Analysis (FEA) is used to learn the spatially varying properties of the substrate from Digital Image Correlation (DIC) measurements of the warpage. The analysis is carried out at room temperature and at an elevated temperature point. Image analysis on electrical artwork is also carried out to correlate the material properties to the substrate metal density. These results will be useful to package and substrate designers to understand how material properties vary over the substrate and how temperature and metal density affect material properties so that robust design for future packages to minimize warpage can be initiated by careful routing of metal lines depending on the locally desired properties of the stack. INDEX TERMS Bayes method, finite element analysis, Markov Chain Monte Carlo (MCMC), material characterization, substrate, warpage.

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Section: Introductionmentioning

confidence: 99%