Proc. SPIE. 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII
KEYWORDS: Edge detection, Detection and tracking algorithms, Data modeling, Matrices, Image processing, Denoising, Scanning electron microscopy, Machine learning, Line edge roughness, Algorithm development
In this paper we propose the implementation of a machine learning technique based on Hidden Markov Models (HMMs) to provide denoising of line edges and unbiased LER measurement. HMMs are widely used with great success in speech recognition and image processing for denoising and filtering noise. Here HMMs are used for similar purposes, working with the observed (noisy) edge data, acquired through SEM imaging and an edge detection algorithm in an effort to retrieve a parent edge signal that is statistically close to the real one.
The developed HMM method was trained with the assistance of synthesized rough edges, on a wide spectrum of predefined and controlled noise levels and roughness characteristics. This ensures the method adapts on a variety of LER parameters and noise levels. The edges were then used to validate its effectiveness in a broad range of line patterns. Our results so far specify the characteristics of the training data set which are required to make the method effective in the unbiased LER measurement.