Analysis and detection of an incorrect profile shape in a classical scatterometric process

Fawzi, Zaki Sabit; Robert, Stéphane; Kalyoubi, Ismail El; Bayard, Bernard

doi:10.1051/epjap/2016160157

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Three types of defects are mainly considered in this work, i.e. top angle (TA), side wall angle (SWA) and rounded angle (RA) [24,44,45]. Further, two typical combinations of these defects were investigated in our experiments, denoted as Model 1 and Model 2 shown in figure 5.…”

Section: Experiments Designmentioning

confidence: 99%

“…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%

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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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Section: Experiments Designmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…The development of artificial intelligence offers exciting new pathways to overcome this bottleneck. However, recent works combining ellipsometry and an artificial neural network (ANN) analysis only focused on the determination of the dielectric function of materials [14][15] or the characterization of the profile of diffraction gratings [16][17].…”

mentioning

confidence: 99%

Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network

et al. 2022

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Due to their tunable physical and chemical properties, alloys are of fundamental importance in material science. The determination of stoichiometry is crucial for alloy engineering. Classical characterization tools such as energy-dispersive x-ray spectroscopy (EDX) are time consuming and cannot be performed in an ambient atmosphere. In this context, we introduce a new methodology to determine the stoichiometry of alloys from ellipsometric measurements. This approach, based on the analysis of ellipsometric spectra by an artificial neural network (ANN), is applied to electrum alloys. We demonstrate that the accuracy of this approach is of the same order of magnitude as that of EDX. In addition, the ANN analysis is sufficiently robust that it can be used to characterize rough alloys. Finally, we demonstrate that the exploitation of ellipsometric maps with the ANN is a powerful tool to determine composition gradients in alloys.

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Experimental identification of a grating profile using neural network classifiers in optical scatterometry

et al. 2021

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In this paper, we develop a new technique, to the best of our knowledge, of grating characterization based on two separate steps. First, an artificial neural network (ANN) is implemented in a classifier mode to identify the shape of the geometrical profile from a measured optical signature. Then, a second ANN is used in a regression mode to determine the geometrical parameters corresponding to the selected geometrical model. The advantage of this approach is highlighted by discussions and studies involving the error criterion that is used widely in scatterometry. In addition, experimental tests are provided on diffraction grating structures with a period of 500 and 750 nm.

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Analysis and detection of an incorrect profile shape in a classical scatterometric process

Cited by 5 publications

References 17 publications

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

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

Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network

Experimental identification of a grating profile using neural network classifiers in optical scatterometry

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