Unsupervised neural network-based image restoration framework for pattern fidelity improvement and robust metrology

Abstract. Background Scanning electron microscope (SEM) images acquired by E-beam tools for inspection and metrology applications are usually degraded by blurring and additive noises. Blurring sources include the intrinsic point spread function of optics, lens aberration, and potential motion blur caused by the wafer stage movements during the image acquisition process. Noise sources include shot noise, quantization noise, and electronic read-out noise. Image degradation caused by blurring and noise usually leads to noisy, inaccurate metrology results. For low-dosage metrology applications, metrology algorithms often fail to obtain successful measurements due to elevated levels of blurring and noise. Image restoration and enhancement are necessary as preprocessing steps to obtain meaningful metrology results. Initial success was obtained by applying neural network-based framework to drastically improve image quality and metrology precision as is demonstrated in the previous work. Aim We aim to provide more details on the neural network model architecture, model regularization, and training dynamics to better understand the model’s behavior. We also analyze the effect of image restoration on key metrology performances such as line edge roughness and mean critical dimension of the patterns. Approach Non-machine learning-based image quality enhancement methods fail to restore low-quality SEM images to a satisfactory degree. More recent convolutional neural networks and vision transformer-based, supervised deep learning models have achieved superior performance in various low-level image processing and computer vision tasks. Nevertheless, they require a huge amount of training data that contain high-quality ground truth images. Unfortunately, high-quality ground truth images for low-dosage SEM images do not exist. Instead, we use self-supervised U-Net combined with a fully connected network (FCN) to recover low-dosage images without the need for ground truth training images. The methodology can be applied to various one- and two-dimensional patterns with different scales, shapes, spatial density, and image intensity statistics. We use image quality metrics and loss function to guide model architecture optimization and study how regularization strength affects the restoration process. These studies provide a better understanding of how the model learns to restore images and how parameters and hyperparameters affect results. Results It is demonstrated that image quality metrics could be successfully used to evaluate self-supervised image restoration process and determine stopping criteria. The restored images show significantly improved image quality and metrology performance. Together, these pave the road to a systematic and automatic implementation of this methodology in real metrology applications. Conclusions A self-supervised U-Net–based model combined with FCN proved itself as a powerful tool to restore highly blurry and noisy low-dosage SEM images. It can be used to improve image quality, suppress metrology noise, and provide more robust measurements. It would become a crucial and necessary preprocessing step for metrology tasks as the E-beam dosage decreases and image quality worsens.