Machine-learning-based error detection modeling and feature scoring for error cause analysis of CD-SEMs

Abstract

The measurement process is important in managing semiconductor device yield, which is affected by the availability of measurement equipment such as critical dimension scanning electron microscopes (CD-SEMs). Here, decreasing CD-SEM availability is caused by measurement errors when inappropriate measurement recipes are used. To improve CD-SEM availability, we developed a machine-learning-based error analysis method to identify error causes and x measurement conditions by using accumulated CD-SEM data. However, a single error analysis model can be applied to only a single semiconductor product because different semiconductor products have different data distribution even if they use the same recipe. Additionally, manufacturers often modify recipes every few months. As a result, an error-cause analysis method needs to be able to easily adapt to new recipes. Therefore, we developed a three-stage method that consists of error detection modeling, feature scoring, and error cause estimation on the basis of high-scoring features. Because we found the top scoring features do NOT change as the feature distribution changes when the error causes are the same, the error cause estimation on the basis of high-scoring features enable to be applied to different semiconductor products and new recipes. We evaluated our method with actual operational data, and estimated error causes that often correspond with the results of manual analysis by skilled engineers.