Layout Feature Extraction Using CNN Classification in Root Cause Analysis of LSI Defects

Abstract

Root cause analysis (RCA) of failures is mandatory to obtain the reliability and productivity of LSIs. Although analyzing layout-induced defects is crucial to optimize design rules and to predict unknown defects, it is a challenging task due to the difficulty in explaining the relationship between defects and circuit layouts. We applied convolutional neural networks (CNNs) to classify LSI layout images to perform the RCA of layout-induced defects in a previous study. However, due to the low resolution of images, actual defect positions were not clearly distinguished. In the present study, we use image clips of different sizes and resolutions for the CNN classification. Experimental results indicate that the validity of the extracted layout features depends on the resolution of image clips. Using the visual explanation technique GradCAM++, the features of defective layouts can be accurately captured in local areas when CNN models are trained on smaller image clips with higher resolution. These layout features included a group of patterns with their surroundings. Conversely, utilizing smaller-size clips deteriorates the classification accuracy due to the incorporation of less information from the images. In the conducted experiments, even in the case of using smaller clips, acceptable performance (the detection rate of defect positions DTR ≅ 90%, and the risk-image classification rate RCR ≅ 10%) can be obtained by increasing the size of training datasets. Partial layouts extracted as features of defective layouts can then be used in RCA and in designing future products.