Proc. SPIE. 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII
KEYWORDS: Signal to noise ratio, Edge detection, Data modeling, Image processing, Denoising, Interference (communication), Scanning electron microscopy, Image filtering, Image denoising, Line edge roughness
Deep Learning (DL) techniques based on Denoising Convolutional Neural Networks (DeCNN) are applied in the denoising of SEM images of line patterns to contribute to noise-reduced (unbiased) LER nanometrology. The models of DeCNN are trained in a sufficiently large set of synthesized SEM images with controlled Gaussian and Poisson noise level. Due to the image-based nature of the DL approach, it can be combined sequentially with the state of the art PSD-based method especially for highly noisy images where the use of the PSD-based method alone fails. The results for test synthesized images show the high predicting capability of the DL assisted method for the commonly used LER parameters and functions (Rms, ξ, α, PSD) of the true (zero-noise) values revealing its potential for future use toward an unbiased LER metrology.