Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Abstract

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.

On the Fly Ellipsometry Imaging for Process Deviation Detection

“…Blanket nitride layers on silicon were first characterized by modelling with an industrial in-line ellipsometry system as already demonstrated in [6]. In this study, a strong inhomogeneity with a diametrical signature was detected in the refractive index of the layer, even though the layers thickness was relatively constant (> 0.05% variation) on the wafer surface (Fig.…”