Deep Learning-Based Visual Recognition for Inline Defects in Production of Semiconductors

Semiconductor manufacturing is a complex process that involves different kind of mechanical, chemical, and photo lithography treatments, such as deposition, etching, and impurity implantation. Quality inspection is vital during such the process, since defects and anomalies introduced during the production process can lead to a variety of failures in later stages. The task of manually or automatically detecting and classifying defects is challenging due to the large variation in design, material, layout, colors, and visual pattern. Existing automated optical inspection methods do not offer flexibility and scalability toward large numbers of defects or new designs. Therefore, visual inspection based on deep learning is considered as a long-term alternative for industry level quality assessment. In this article, automated visual defect identification in patterned wafers during the production process is proposed. By employing transfer learning, we trained and evaluated a number of convolutional neural network architectures including VGG, ResNet, and dense convolutional network (DenseNet), over a range of parameters for a binary and multiclass classification and determined optimal architecture. The experimental evaluation is carried out on real industrial data of patterned wafers comprising of nine different defect types and from 14 production steps. The results show that the network based on DenseNet201 achieves the highest performance up to 98.17% on binary classification and 96.04% on multiclass classification.