Denoising sample-limited SEM images without clean data

Over the past few years, noise2noise, noise2void, noise2self, and unsupervised deep-learning (DL) denoising techniques have achieved great success, particularly in scenarios where ground truth data is not available or is difficult to obtain. For semiconductor SEM images, ground truth or clean target images with lower noise levels can be obtained by averaging hundreds of frames at the same wafer location, but it is expensive and can result in physical damage to the wafer. This paper’s scope is to denoise SEM images without clean target images and with limited image counts. Inspired by noise2noise, we proposed an additive noise algorithm and DL U-net. We achieved good denoising performance using a limited number of noisy SEM images, without the clean ground truth images. We proposed the “denoise2next” and “denoise2best”. We compared generative adversarial network(GAN) generated images and Additive noise images for data augmentation. This paper further quantified the impact of image noise level, pattern diversity, and continuous (aka transfer) learning. The data sets used in the work include both line/space and logic pattern.