@inproceedings{53adcd7b71b14d688d7ff83427164046,

title = "CNN-based Stochastic Regression for IDDQ Outlier Identification",

abstract = "In order to reduce DPPM (defect parts per million), IDDQ testing methodology can be exploited for identifying {"}outliers{"} which are potentially defective but not detected by signoff 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 this paper, by employing a stochastic regression model, the mean as well as the variance of the IDDQ of a die under test (DUT) can be predicted. According to the predicted mean and variance, we derive an expected IDDQ range and identify the DUT as an outlier if its actual IDDQ measurement is beyond the expected range. The proposed stochastic regression model is obtained by training a convolutional neural network (CNN) and, based on its primitive property of convolutional kernel mapping with large volume of industrial data, spatial correlations (due to spatially-correlated process variations, etc) can be considered/captured. The trained data-driven CNN is highly accurate in terms of R-square (0.958) and RMSE (0.783), and the percentage of identified outliers (0.047%) is very close to the theoretical reference (0.050%), which validates the efficacy of our proposed methodology.",

author = "Chen, {Chun Teng} and Yen, {Chia Heng} and Wen, {Cheng Yen} and Yang, {Cheng Hao} and Wu, {Kai Chiang} and Mason Chern and Chen, {Ying Yen} and Kuo, {Chun Yi} and Lee, {Jih Nung} and Kao, {Shu Yi} and Chao, {Mango Chia Tso}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 38th IEEE VLSI Test Symposium, VTS 2020 ; Conference date: 05-04-2020 Through 08-04-2020",

year = "2020",

month = apr,

doi = "10.1109/VTS48691.2020.9107570",

language = "English",

series = "Proceedings of the IEEE VLSI Test Symposium",

publisher = "IEEE Computer Society",

booktitle = "Proceedings - 2020 IEEE 38th VLSI Test Symposium, VTS 2020",

address = "United States",

}