Mr. Seoungho Gwak Profile

Seoungho Gwak

at SAMSUNG Electronics Co Ltd

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Proceedings Article | 20 June 2021 Presentation + Paper

Machine learning aided profile measurement in high-aspect-ratio nanostructures

Inho Kim, Yoonsung Bae, Seoungho Gwak, Euiseok Kum, Taeyong Jo

Proceedings Volume 11783, 117830K (2021) https://doi.org/10.1117/12.2592282

KEYWORDS: Nanostructures, Data modeling, Machine learning, Scanning electron microscopy, 3D modeling, Signal processing, Semiconductors, Performance modeling, Nondestructive evaluation, Image processing

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The trend to produce semiconductor devices having more complex nanostructures results in the increasing importance of exquisite systems measuring multiple Critical Dimensions (CDs) of nanostructures. However, from a practical point of view, it is difficult to apply conventional methodologies to mass production because of cost and complexity issues. In this study, we propose an application of machine learning techniques utilizing optical information to measure nanoscale profiles of channel holes in High-Aspect-Ratio (HAR) structure of vertical NAND flash, which is applicable to mass production. By combining the conventional methodologies, the proposed method yields data pairs for supervised learning which include optical spectra obtained with Rotating Polarizer Ellipsometer (RPE) and images obtained with Scanning Electron Microscopy (SEM). Several preprocessing steps and machine learning techniques are introduced to train a model with sufficient performance to be applicable to mass production. In experiments, we obtained a model with coefficient of determination (R²) of 0.8 and Root Mean Square Error (RMSE) of 1.3 nm when predicting hundreds of nanoscale profiles of the channel holes which are measured with SEM. Furthermore, we confirmed that only 500 samples of data are sufficient to achieve the model performance with R² greater than 0.7 and RMSE less than 1.5 nm. The proposed method is capable of replacing the conventional methods of profile measurement in the mass production stage by reducing the cost of destructive methods and accurately measuring the profiles of complex nanostructures without theoretical modeling.

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