Quantifying CD-SEM contact hole roughness and shape combined with machine learning-based pattern fidelity scores for process optimization and monitoring

Abstract

Process window qualification using focus-exposure wafers is an essential step in lithography and a key use case for CD-SEM metrology. An automated analysis using the correlation between CD and focus/dose is easily possible but rarely done due to missing safety checks. Pattern fidelity that is analyzed by eye and problematic focus/dose conditions that may cause pattern degradation are excluded by hand. Specifically, when EUV lithography is utilized for exposing the most critical layers, roughness estimation becomes much more important, as it will restrict the process window further. We develop and describe unbiased and stable roughness estimates for contact hole patterns and integrate them into the process window analysis pipeline and inline monitoring routine. The analysis goes beyond simple roughness values and can detect a variety of possible CD-SEM measurement problems and shape deviations as well. Furthermore, we introduce a novel image-based machine-learning approach to detect outliers and quantify defective or abnormal patterns. Notably, the underlying model does not require knowledge of the types of CD features or design information for which outliers should be detected. We demonstrate that the approach can reliably detect local defects and a variety of other pattern anomalies. Using the generated visualizations, images with anomalous features can be flagged automatically and the locations of the defects or deviations are pinpointed. The approach yields not only the final missing piece in automated process window qualification, but also new opportunities to monitor pattern fidelity in lithographical semi-conductor processes.