CNN-Based Stochastic Regression for IDDQ Outlier Identification

To reduce DPPM (defect parts per million) on IC products, IDDQ testing can be exploited for identifying the outliers which are potentially defective but not detected by sign-off functional and parametric tests. Conventional IDDQ testing paradigms depending on a simple statistical 6σ rule or engineers’ experience are usually too conservative to effectively identify non-trivial outliers, especially when spatial correlations are of great concern/influence. In the paper, an improved CNN-based method can be proposed for IDDQ outlier identification. In the proposed method, the mean and the standard deviation on the IDDQ value inside a die under test (DUT) can be predicted by employing a stochastic regression model. According to the predicted mean and standard deviation, we derive an expected IDDQ interval and identify the DUT as an outlier if its actual measured IDDQ value is beyond the expected interval. From the observation of the experimental results, the improved data pre-processing and the improved CNN-based stochastic regression can be contained to enhance the prediction accuracy of the expected IDDQ intervals. In the improved method, the spatial correlations of the neighboring dice inside a window can be considered by training a CNN-based stochastic regression model with a large volume of industrial data on 28nm and 65nm products. The trained model is highly-accurate prediction in the R (0.973) and RMSE (0.626 mA) of the expected IDDQ values on 28nm product and the R (0.942) and RMSE (2.155 uA) of the expected IDDQ values on 65nm product. Furthermore, the experimental results show that the trained model can capture the potential defective dice by identifying efficient IDDQ outliers.