Machine learning and hybrid metrology using HV-SEM and optical methods to monitor channel hole tilting in-line for 3D NAND wafer production

Michael Meng; Leeming Tu; Jian Mi; Haydn Zhou; Xi Zou

doi:10.1117/12.2551622

20 March 2020 Machine learning and hybrid metrology using HV-SEM and optical methods to monitor channel hole tilting in-line for 3D NAND wafer production

Michael Meng, Leeming Tu, Jian Mi, Haydn Zhou, Xi Zou

Author Affiliations +

Proceedings Volume 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV; 113250O (2020) https://doi.org/10.1117/12.2551622
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

Tilted channel holes affect final yield significantly in High Aspect Ratio (HAR) 3D NAND memory wafer processing. An in-line measurement method is developed to use machine learning that utilizes the spectra from optical metrology to map Tilt-X and Tilt-Y. Reliable reference is provided by high voltage SEM. Results show that the correlation of optical and HV e-Beam measurements has R² more than 0.92. In addition, measurement throughput is improved tremendously by 40% from e-Beam to optical metrology. Combined with other optical metrology on the same platform (thickness, and Optical CD), this method is much efficient for in-line tilt measurement after channel hole etch process.

Conference Presentation

Citation Download Citation

Michael Meng, Leeming Tu, Jian Mi, Haydn Zhou, and Xi Zou "Machine learning and hybrid metrology using HV-SEM and optical methods to monitor channel hole tilting in-line for 3D NAND wafer production", Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113250O (20 March 2020); https://doi.org/10.1117/12.2551622

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