Using Machine Learning Method to Improve Design Sampling Efficiency for Fab applications

A prevalent practice in semiconductor fabrication involves the utilization of Graphic Data Systems (GDS) for sampling within the Fab. However, with the reduction in process nodes, the capacity and intricacy of GDS escalate, making efficient and accurate sampling increasingly imperative. The emphasis on sampling varies across diverse application scenarios within the factory. This article delves into the application of machine learning methods to enhance sampling efficiency for Fab applications. It encompasses a spectrum of applications, notably the Photo Resist change project, where machine learning-based sampling techniques are deployed to streamline inspection points. Additionally, the article investigates the potential of machine learning in mask Critical Dimension (CD) performance verification, facilitating real-time monitoring of mask performance through optimized sampling strategies. Moreover, the implementation of SEM down sampling in the defect review process, driven by machine learning, demonstrates the capacity to boost defect hit rates and proficiently identify missing real defects.