SEM image denoising with unsupervised machine learning for better defect inspection and metrology

CD-SEM images inherently contain a significant level of noise. This is because a limited number of frames are used for averaging, which is critical to ensure throughput and minimize resist shrinkage. This noise level of SEM images may lead to false defect detections and erroneous metrology. Therefore, reducing noise in SEM images is of utmost importance. Both conventional noise filtering techniques and recent most discriminative deep-learning based denoising algorithms are restricted with certain limitations. The first enables the risk of loss of information content and the later mostly requires clean ground-truth or synthetic images to train with. In this paper, we have proposed an U-Net architecture based unsupervised machine learning approach for denoising CD-SEM images without the requirement of any such ground-truth or synthetic images in true sense. Also, we have analysed and validated our result using MetroLER, v2.2.5.0. library. We have compared the power spectral density (PSD) of both the original noisy and denoised images. The high frequency component related to noise is clearly affected, as expected, while the low frequency component, related to the actual morphology of the feature, is unaltered. This indicate that the information content of the denoised images was not degraded by the proposed denoising approach in comparison to other existing approaches.