Through the technique, Sampling and Prediction of Lithography Overlay Errors, the cost and time of overlay error measurement can be reduced to improve the process efficiency. We identify key sampling through clustering and machine learning models, and design a new sampling algorithm in photolithography process. Due to the complexity of wafer and many training factors of wafer data, we combine the clustering algorithm with incremental learning to meet customers＇ unique needs and achieve the goal of optimally sampling and reducing the costs.

Due to the lack of a large amount of wafer measurement data in practice, we combine the clustering with incremental learning to meet customers＇ unique needs. Moreover, we develop a dynamic clustering algorithm to automatically identify the correct number of clusters for different types of attributes, and gradually build learning models with a few training data to predict wafer exposure and compensation. While repeatedly learning new data, it also retains the knowledge of previous models. In addition, we show the new sampling algorithm outperforms the commercial tool by 10％ under key measures.

We develop a new sampling algorithm with our partner companies to analyze the common features of overlay by dynamic clustering. We select testkeys through the incremental learning model, and predict the actual exposure of entire wafers. We also extend the sampling technique to the testing process of Wafer Probe to reduce the cost of the testing process. In addition to developing relevant forward-looking technologies, it is expected to enhance Taiwan’s cutting-edge technology and raise the world competitiveness in the semiconductor manufacturing industry.

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