Accurate Failure Rate Prediction Based on Gaussian Process Using WAT Data

Abstract

In this paper, we propose a novel method for predicting the characteristic failure rate from a small amount of data with high accuracy using the posterior distribution of the Gaussian process. In the proposed method, using multiple lots, a local pattern on the wafers is estimated from the measurement results of the target-probe test item for failure-rate prediction. For failure-rate prediction, the global trend of each wafer is predicted by the Gaussian process using WAT data and superimposed on the local pattern. The proposed method derives the failure rate of each die based on the posterior distribution using the Gaussian process in the global trend calculation. Experiments using industrial semiconductor manufacturing data demonstrate that the proposed method can reduce the estimation error by approximately 70% compared to a conventional method.

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