Evaluating and predicting the failure rate of interconnect is a time consuming and expensive process. Non parametric techniques to analyze the qualification data, such as employing the Chi-square distribution require large sample sizes to achieve an accurate estimate.

Recently there has been a resurgence in the use of extreme value theory (EVT). Increases in temperature records, the numbers of strong storms, and flooding events have fueled this interest. A novel method that is based on EVT and an accelerated degradation model for estimating the failure rate from a set of stress data is proposed and described.

There are many advantages of this technique, and recommendations on sample size are discussed. Advice is given as to how the total sample should be sectioned before the maximum is taken of each subset. Interconnect examples are given, generated from Monte Carlo simulations of known distributions, and used for a comparison of the extreme value technique versus Chi-square and Johnson distribution methods.

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