Reconstruction of a complex profile shape by weighting basic characterization results for nanometrology

Tchéré, Moustapha Godi; Robert, Stéphane; Fawzi, Zaki Sabit; Bayard, Bernard

doi:10.1364/ao.58.006118

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…An artificial neural network (ANN) was developed and trained to determine the grating parameters from the diffraction efficiencies measured using a spectral scatterometer. Studies where ANNs have been used in the context of scatterometry include [7][8][9][10][11][12][13][14][15]. These span a time period from 1999 up to 2022, and the increase in computational capacity during the period is clearly visible.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network assisted spectral scatterometry for grating quality control

Mattila,

Nysten,

Heikkinen

et al. 2024

Meas. Sci. Technol.

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Spectral scatterometry is a technique that allows rapid measurements of diffraction efficiencies of diffractive optical elements (DOEs). The analysis of such diffraction efficiencies has traditionally been laborious and time consuming. However, machine learning can be employed to aid in the analysis of measured diffraction efficiencies. In this paper we describe a novel system for providing measurements of multiple measurands rapidly and concurrently using a spectral scatterometer and an Artificial Neural Network (ANN) which is trained utilizing transfer learning. The ANN provides values for the pitch, height, and line widths of the DOEs. In addition, an uncertainty evaluation was performed. In the majority of the studied cases, the discrepancies between the values obtained using a scanning electron microscope (SEM) and artificial neural network assisted spectral scatterometer (ANNASS) for the grating parameters were below 5 nm. Furthermore, independent reference samples were used to perform a metrological validation. An expanded uncertainty ($k = 2$) of 5.3 nm was obtained from the uncertainty evaluation for the measurand height. The height value measurements performed employing ANNASS and SEM are demonstrated to be in agreement within this uncertainty.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network assisted spectral scatterometry for grating quality control

Mattila,

Nysten,

Heikkinen

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Moreover, the existing studies show that neural network driven models and techniques play important roles in such research tasks. In addition to the works above, there are also studies that utilizes neural network models to investigate the optical response of micro-to-nanoscale grating structures with defects and to reconstruct the profiles [24][25][26]. The grating or grating-like structures have similar topography and properties with the single layer DBO targets.…”

Section: Introductionmentioning

confidence: 99%

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Wang,

Meng,

Zhang

et al. 2024

Meas. Sci. Technol.

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Overlay (OVL) is one significant performance indicator for the lithography process control in semiconductor manufacturing. The accuracy of the OVL metrology is extremely critical for guarantee the lithography quality. Currently, diffraction-based overlay (DBO) is one of the mainstream OVL metrology techniques. Unfortunately, the accuracy of the DBO metrology is largely affected by the defect features of the overlay target. Therefore, there is a strong need to investigate the impacts of these target defects on the DBO metrology performance. However, efficiently investigating the statistical and interactive impacts of various DBO target defects remains challenging. This study aims to address this issue through proposing an intelligent sensitivity analysis approach. A cumulative distribution based Global Sensitivity Analysis (GSA) method is utilized to assess the nonlinear influences of multiple defects in the OVL target on the DBO inaccuracy. The scenarios with both known and unknow distributions of the OVL target defects are considered. For the former, a neural network driven forward model is constructed for fast calculating the optical diffraction responses to accelerate the GSA process. For the latter, another neural network based inverse model are built for efficiently estimating the distribution of the target defects. Finally, a series of simulation experiments are conduct for typical DBO targets with multiple common defect features. The results demonstrate the effectiveness and robustness of the proposed approach as well as give valuable insights into the DBO defect analysis. Our study provides a strong tool to assist the practitioners in achieving intelligent and efficient DBO analysis and thus in enhancing OVL metrology performance.

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Machine learning aided solution to the inverse problem in optical scatterometry

et al. 2022

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Reconstruction of a complex profile shape by weighting basic characterization results for nanometrology

Cited by 5 publications

References 21 publications

Artificial neural network assisted spectral scatterometry for grating quality control

Artificial neural network assisted spectral scatterometry for grating quality control

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Machine learning aided solution to the inverse problem in optical scatterometry

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