1. Warranty Forecasting of Electronic Boards using Short- term Field Data Mustafa Altun, PhD Assistant Professor Istanbul Technical University www.ecc.itu.edu.tr 11.07.2 014

2. Outline  MOTIVATION Overview of reliability prediction techniques. Unexpected failures in the field.  GOAL Warranty forecasting using short-term data.  METHOD Modeling for an old board.  Filtering the data and developing the model. Prediction for a new board.  Estimation methods: maximum likelihood, Bayesian, rank regression.  RESULTS

3. Outline  MOTIVATION Overview of reliability prediction techniques. Unexpected failures in the field.  GOAL Warranty forecasting using short-term data.  METHOD Modeling for an old board.  Filtering the data and developing the model. Prediction for a new board.  Estimation methods: maximum likelihood, Bayesian, rank regression.  RESULTS

4. Overview of Reliability Prediction Field Data Analysi s Accelerated Tests Simulation with PoF Pass-Fail Tests ACCURACY TEST DURATION REAL-TIME PERFORMANCE PREDICTION BEFORE FIELD COST Red cells for the worst Blue cells for the best White cells for the average

5. Unexpected Failures in the Field. Unexpected failure rates in «Early Failure». How to predict the future? What is the proper amount of time that the product should stay in the field?

6. Prediction with Field Return Data Conventioanlly, «useful life» or «wear out» period can not be predicted using the data from «early failure». We overcome this problem! Goal: predicting reliability of electronic boards throughout their 3-year warranty with 3-month field data.

7. Outline  MOTIVATION Overview of reliability prediction techniques. Unexpected failures in the field.  GOAL Warranty forecasting using short-term data.  METHOD Modeling for an old board.  Filtering the data and developing the model. Prediction for a new board.  Estimation methods: maximum likelihood, Bayesian, rank regression.  RESULTS

8. Modeling for an Old Board  Use full field data for an old electronic board.  Filter the data to eliminate incomplete and poorly collected data. Based on Weibull β parameter. Targets on hidden errors  Model the filtered data with Weibull distribution. β values for different assembly times vs. assembly time intervals

9. Modeling for Weibull β Parameter Modeling for β: 1.Creating a model.  product dependent parameter  technology dependent parameter

10. Prediction for a New Board Prediction method: Bayesian fitting for Weibull (prior exponential) β parameter MLE vs. Bayesian  Weibull distribution is used  Bayesian overwhelms MLE for small samples.

11. Prediction for a New Board Reliability prediction for β: 1.Creating a model 2.Using the model for prediction of a new board.  Use same.  Determine using 3-month data of the new board.

12. Experimental Results

13. Board-K is not a member of the family of Board B-E-F.

14. Conclusions – Future Work  We make a 3-year warranty forecasting of a new electronic board having its 3-month field data.  We develop a mathematical model of β as a function of field data time interval using board dependent parameters.  The predicted results from our method and the direct results from the field return data matches well.  We will improve our model, more generic, to be applicable for different electronic products.

15. Thank you!