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Thermo-mechanical Models For Life Prediction Of CBGA Packages.

Thermo-mechanical Models For Life Prediction Of CBGA Packages. Data Set. Data collected from 10 references . [ Andy Perkins, Raj.N.Master, Donald.R.Banks, Bor Zen Hong, Burnette.T, Mukta Farroq, Sung.K.Kang, David. Gerke, IBM user guide.] 98 data points have been collected.

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Thermo-mechanical Models For Life Prediction Of CBGA Packages.

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  1. Thermo-mechanical ModelsFor Life Prediction Of CBGA Packages.

  2. Data Set • Data collected from 10 references. [ Andy Perkins, Raj.N.Master, Donald.R.Banks, Bor Zen Hong, Burnette.T, Mukta Farroq, Sung.K.Kang, David. Gerke, IBM user guide.] • 98 data points have been collected. • Data includes • High lead solder and lead free (SAC). • High CTE substrate and low CTE. • Underfilled and non underfilled packages. • N50 has been used as rersponse. • Predictor variables include, die length, die width, die area, diagonal length, ball pitch, ball count, ball diameter, substrate CTE, substrate thickness, solder type, underfill modulus and CTE, thermal cycling conditions, PCB type and thickess.

  3. Model Input Selection. • Subset of input variables selected using stepwise and method of best subsets. • Stepwise methods resulted in 10 variables as potentially important. • Input variables selected are diagonal length, ball count, ball dia, substrate thickness, substrate CTE, PCB thickness, solder type, underfill modulus , underfill CTE and delta T. • Prediction model has been developed on the input set using multiple linear regression.

  4. Multiple Linear Regression

  5. Model Adequacy Checking

  6. Model Adequacy Checking • Normality is clearly evident from normal probability plot and plot of histogram. • Variance is almost constant. • All assumptions of the model are satisfied. • Ball count and diagonal length are found to be correlated however , the problem is not very serious. • Model described 92% of the information in the dataset.

  7. Natural Log Transformed Model • Predictor and response variables have been transformed using natural log transformation. • Objective of transformation is to determine power law dependence and compare them with Box Tidwell values.

  8. Natural Log Transformed Model

  9. Model Adequacy Checking

  10. Model Adequacy Checking • Normality is clearly evident from normal probability plot and plot of histogram. • Variance is almost constant. • All assumptions of the model are satisfied. • Model described 92% of the information in the dataset

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