Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Spectroscopic ellipsometry is a very sensitive metrology technique to accurately measure the thickness and the refractive index of the different layers present on specific dedicated metrology targets. In parallel, optical defectivity techniques are widely implemented in production lines to inspect a large number of dies and catch physical and patterning defects during the process flow. It becomes then of interest to explore a new approach overlapping metrology and defectivity by using the sensitivity of metrology tools on a full wafer scale. In our case, spectroscopic ellipsometry's optical response was collected directly on the dies to capture specific deviations such as film properties and thickness variation. This is an innovative strategy that requires a model-less approach, combining an automatic ellipsometry mapping generation and a smart classification via a machine learning algorithm. In this paper, we will present such approach on two industrial use cases and explain how an image classification algorithm can be implemented to automatically detect the process drift on the latter.