A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Dey, Bappaditya; Wu, Stewart; Blanco, Victor; bhamidipati, Samir; Essam, Amr; Ahi, Kevin; Halder, Sandip; Fenger, Germain

doi:10.1117/12.2680884

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Universal denoiser to improve metrology throughput

Kim,

Byun

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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The semiconductor manufacturing process is a complex and highly technical operation, demanding precise and consistent metrology throughout its prolonged duration. As manufacturing processes grow in complexity, it becomes imperative to measure key variables of the intermediate products. Consequently, there is an increased demand for higher throughput in metrology to enhance measurement capacity. The electron-beam (E-Beam) metrology tools can be accelerated by integrating denoiser models for image enhancement and reducing the sample rate of raw images. We propose a novel universal denoising method specifically tailored for the semiconductor industry. Distinct from traditional denoisers designed for standard cameras and the RGB space, our proposed solution is tailored to the nano-scale structures and noise patterns inherent in semiconductor images obtained via E-beam tools. To meet the strict precision requirements of semiconductor metrology, where even minimal deviations carry substantial implications, our approach introduces a specialized network structure with novel loss functions that reflect the unique characteristics of semiconductor metrology. We propose a novel conditioning scheme to apply a single trained model to diverse image domains from various wafer products and layers. By combining the proposed loss functions and conditioning scheme, we can universally handle multiple image domains with a single model. This method significantly reduces time expenditure while preserving the crucial accuracy necessary for high-quality semiconductor production. Through comprehensive volume testing across diverse metrology recipes, which covers the entire spectrum of DRAM fabrication, our method has demonstrated a notable increase in metrology throughput, achieving an average enhancement of 26%, and reaching up to 46%, all without compromising accuracy or reliability. This breakthrough offers a versatile and efficient solution, marking a significant advancement in the field of semiconductor manufacturing.

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