A Cluster-Based Method for Quantitative Characterization and Uncertainty Evaluation of Areal Step Height in Structured Surface Metrology

Abstract

Quantitative characterization and uncertainty evaluation of areal step height is of increasing importance for semiconductor manufacturing. However, step characterizations often confront problems of repeatability and reproducibility due to fitting of upper and lower planes. To solve this problem, we propose a cluster-based method for step height characterization and uncertainty evaluation. By data reconstruction and K-means clustering, our method converts characterizing steps into approximating Euclidean distances, without necessity to fit planes. Moreover, it can evaluate uncertainties simultaneously with parameterization. The proposed method is firstly validated with synthetic data. Then two experiments respectively on nominal 5 μm and 90 nm standard artefacts are carried out. The characterization results highly conform to the calibrated values, with 0.0986% and 1.22% differences respectively. Comparing to the latest ISO method, the cluster-based method presents better performance on repeatability and reproducibility. The experimental results demonstrate that the method works well for measurement either with or without outliers.