Ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry for semiconductor metrology

We propose an ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry (IMMSE) system for semiconductor metrology. The IMMSE system achieves large-area measurements with a 20 mm × 20 mm field of view (FOV)-the largest FOV reported to date-and a spatial resolution of 6.5 µm. It enables the acquisition of over 10 million Mueller matrix (MM) spectra within the FOV, while a unique signal correction algorithm ensures spectrum consistency across the FOV. Leveraging this numerous MM spectra and machine learning, spatially dense metrology across the entire wafer area is achieved. This approach provides over 1987 times more metrology data and 662 times higher throughput compared to conventional point-based methods, such as scanning electron microscopy. We experimentally demonstrate the potential of the IMMSE for yield enhancement in semiconductor manufacturing by identifying spatial variations of dynamic random access memory (DRAM) structures within individual chips as well as across the wafer.

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Publication Details

Journal
Nature Communications
Published
2025-09-26
DOI
https://doi.org/10.1038/s41467-025-63511-1
Citations
2
Primary Topic
Optical Polarization and Ellipsometry
Type
article
Field-Weighted Citation Impact
1.35

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Ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry for semiconductor metrology

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Ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry for semiconductor metrology

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article en
2 citations

Abstract

We propose an ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry (IMMSE) system for semiconductor metrology. The IMMSE system achieves large-area measurements with a 20 mm × 20 mm field of view (FOV)-the largest FOV reported to date-and a spatial resolution of 6.5 µm. It enables the acquisition of over 10 million Mueller matrix (MM) spectra within the FOV, while a unique signal correction algorithm ensures spectrum consistency across the FOV. Leveraging this numerous MM spectra and machine learning, spatially dense metrology across the entire wafer area is achieved. This approach provides over 1987 times more metrology data and 662 times higher throughput compared to conventional point-based methods, such as scanning electron microscopy. We experimentally demonstrate the potential of the IMMSE for yield enhancement in semiconductor manufacturing by identifying spatial variations of dynamic random access memory (DRAM) structures within individual chips as well as across the wafer.

Nature CommunicationsVol. 16(1)
Samsung (South Korea) (KR)
Samsung
Openalex Percentile: Top 17%
Optical Polarization and Ellipsometry
1.35
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