Sparsity-Based Super Resolution for SEM Images

Abstract

The scanning electron microscope (SEM) is an electron microscope that produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is formed. Since its invention in 1942, the capabilities of SEMs have become paramount in the discovery and understanding of the nanometer world, and today it is extensively used for both research and in industry. In principle, SEMs can achieve resolution better than one nanometer. However, for many applications, working at subnanometer resolution implies an exceedingly large number of scanning points. For exactly this reason, the SEM diagnostics of microelectronic chips is performed either at high resolution (HR) over a small area or at low resolution (LR) while capturing a larger portion of the chip. Here, we employ sparse coding and dictionary learning to algorithmically enhance low-resolution SEM images of microelectronic chips-up to the level of the HR images acquired by slow SEM scans, while considerably reducing the noise. Our methodology consists of two steps: an offline stage of learning a joint dictionary from a sequence of LR and HR images of the same region in the chip, followed by a fast-online super-resolution step where the resolution of a new LR image is enhanced. We provide several examples with typical chips used in the microelectronics industry, as well as a statistical study on arbitrary images with characteristic structural features. Conceptually, our method works well when the images have similar characteristics, as microelectronics chips do. This work demonstrates that employing sparsity concepts can greatly improve the performance of SEM, thereby considerably increasing the scanning throughput without compromising on analysis quality and resolution.

Resolution enhancement in scanning electron microscopy using deep learning

“…Although computational approaches for super resolution in electron microscopy have been previously demonstrated 12,13 , they require that a portion of the image be taken in high resolution or that the images have similar characteristics and contain sparse unique structures outside of a periodic topology. Other computational enhancements that have been applied to SEM images include denoising as well as deconvolution to reduce the spatial blur in the image caused by the finite beam size 14,15 .…”

Universal denoiser to improve metrology throughput

“…SEM images are a critical input source for metrology and inspection in semiconductor processes, and efforts 1,2 to improve the quality of SEM images have been ongoing for an extended period. Moreover, recent efforts [3][4][5] have seen the emergence of deep learning-based methods as well.…”

“…Several studies [1][2][3][4][5] have been introduced to enhance images obtained from E-beam tools in the semiconductor field, most of which are inspired by general image denoisers designed for the RGB space and standard cameras in Figure 1: Overview of our method; the proposed universal denoiser takes low-framed images (Raw-N l F) as input and outputs high-framed images (Predicted-N h F), where N l is the number of frames in the low-framed input and N h is the number of frames in the high-framed output, designed to match the ground truth high framed images (GT-N h F). The input images can come from a variety of multiple domains, and our method effectively and robustly processes them through our novel conditioning scheme.…”

An Adversarial Learning Approach for Super-Resolution Enhancement Based on AgCl@Ag Nanoparticles in Scanning Electron Microscopy Images

“…Among them, SEM is the most versatile and commonly used in this field. By launching a focused electron beam at a specimen, and detecting the electron emission of the sample, SEM achieves high spatial resolution images containing topographic and compositional information of the surface [13]. It also has fast acquisition and large magnification, which make it convenient for real-time high-sensitivity digital processing [14].…”