KEYWORDS: 3D modeling, 3D applications, Semiconductors, Deep learning, Metrology, Algorithm development, Mathematical optimization, Evolutionary algorithms, 3D metrology, Software development
This paper introduces our development of software designed for OCD (Optical Critical Dimension) modeling, utilizing 3D graphics design functionality. In the OCD metrology, the role of analysis software is crucial for accurately and precisely extracting CD parameters from intricate device structures. Our software incorporates calculation engines grounded in Physics and Machine Learning - RCWA (Rigorous Coupled Wave Analysis) and DL (Deep Learning). The software's advanced 3D modeling engine supports complex structure manipulation and precise adjustments of a broad range of parameters, including optical properties. This facilitates detailed device geometry exploration through a cohesive interface. The DL algorithm has been developed ensuring consistency between RCWA and DL predictions, essential for accurate and rapid OCD metrology. We have conducted a comprehensive evaluation process to assess the consistency between RCWA calculations and 3D representations, encompassing both 2D and 3D structures. Further evaluation is planned, specifically focusing on real patterned wafers.
We investigate on how to improve the performance of thickness determination from the optical scatterometry spectrum using machine learning. Our investigation is focused on a specific application for thick layered structures for 3D NAND with oxide/nitride repeating pairs. Since fast determination of thickness of every layer or detection of any outlier is not very efficient with the regression analysis using an optical model-based calculation due to requirement of a huge amount of calculations along with many parameters, machine learning (ML) can be applied for this application pursuing a faster solution. However, we also need to achieve its precision and accuracy as good as possible under a limited amount of ML train data sets. In order to carry out an efficient extraction or selection of features from data which is very important for improved performance of ML, we applied Fourier analysis of spectrum and investigate on how ML performance is improved.
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