Deep-learning-based SEM image denoiser (Conference Presentation)

Deep-learning-based SEM image denoiser Dorin Cerbu1, Sandip Halder1, Philippe Leray1 1IMEC, Kapeldreef 75, B-3001 Leuven, Belgium We report the development of a new method to denoise SEM images with the help of artificial neural networks. Upon using a preprocessing and training scheme tailored for SEM images of structures, most often encountered in semiconductor manufacturing, we can efficiently denoise images affected with varying degrees of noise severity and origin. In the figure below, we show an example of how we can use this filter efficiently to treat noisy images and improve the image quality. This can help in acquisition of more stable and better metrology data. Fig1(a) original image (b) Image which has been denoised using deep-learning based algorithms This development is of utmost importance for the case of post-litho processing step where resist nanostructures when SEM inspected are usually impacted by the electron beam and shrink, hence skewing critical dimension measurements. This is especially true as we push towards sub N-10 nm nodes. Application of our deep-learning processing scheme allows efficient noise reduction on SEM inspection images and helps us discern minor details previously shadowed by noise. This is extremely important as we move towards using EUV in high volume manufacturing. Small details can be crucial to understand the root-cause of stochastic and process defects. In previous work, we have already shown different approaches to understand stochastic defects [1-2]. The goal of this work is to enhance the image quality as much as possible to gain further fundamental understanding on nano-defects. [1] S. Halder et. al., ‘Using machine learning techniques to understand EUV stochastics, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018 [2] K. Sah et.al., ‘EUV stochastic defect monitoring with advanced Broadband optical wafer inspection and e-Beam review systems’, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018