A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map

Abstract

The semiconductors are used as various precision components in many electronic products. Each layer must be inspected of defect after drawing and baking the mask pattern in wafer fabrication. Unfortunately, the defects come from various variations during the semiconductor manufacturing and cause massive losses to the companies' yield. If the defects could be identified and classified correctly, then the root of the fabrication problem can be recognized and eventually resolved. Automatic optical inspection (AOI) is used to visualize defect patterns and identify root causes of die failures. AOI can be replaced a large number of human inspections with high-speed and accurate inspection technology, to achieve consistency in the detection and shorten the inspection time, then improve product quality and competitiveness. The defect is judged from the feature in AOI, but the final goal is to determine if the defect is a true or a pseudo defect of the wafer. Then, we need to determine what defect type is. But the current AOI needs a subsequent final verification by the human to judge the type of defect. Machine learning (ML) techniques have been widely accepted and are well suited for such classification and identification problems. In this paper, we employ convolutional neural networks (CNN) and extreme gradient boosting (XGBoost) for wafer map retrieval tasks and the defect pattern classification. CNN is the most famous deep learning architecture. The recent surge of interest in CNN is due to the immense popularity and effectiveness of convnets. XGBoost is the most popular machine learning framework among data science practitioners, especially on Kaggle, which is a platform for data prediction competitions where researchers post their data and statisticians and data miners compete to produce the best models. CNN and XGBoost are compared with a random decision forests (RF), support vector machine (SVM), adaptive boosting (Adaboost), and the final results indicate a superior classification performance of the proposed method. Our experimental result demonstrates the success of CNN and extreme gradient boosting techniques for the identification of defect patterns in semiconductor wafers. The overall classification accuracy for the test dataset of CNN and extreme gradient boosting is 99.2%/98.1%. We demonstrate the success of this technique for the identification of defect patterns in semiconductor wafers. We believe this is the first time accurate computational classification in such task has been reported achieving accuracy above 99%.