Artificial-neural-network-assisted DUV scatterometry for OCD on HAR sub-micron structures

Zih-Ying Fu; Wei-Hsin Chein; Fu-Sheng Yang; Liang-Chia Chen

doi:10.1117/12.2657642

27 April 2023 Artificial-neural-network-assisted DUV scatterometry for OCD on HAR sub-micron structures

Zih-Ying Fu, Wei-Hsin Chein, Fu-Sheng Yang, Liang-Chia Chen

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 124960E (2023) https://doi.org/10.1117/12.2657642
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

A neural network-assisted spectral scatterometry method is presented to measure multi-dimensional critical dimensions (CDs) on high aspect ratio (HAR) structures with micron or submicron scales. With the rise of 3D integrated circuit packaging, there is a need for accurate characterization of HAR sub-micron structures. This method uses DUV scatterometry and a broadband light source from DUV to visible light to gather multi-channel reflection data. The inverse modeling method and artificial neural network model enable accurate measurement of multiple CDs of test structures. The results showed accurate measurement of deep trench critical dimensions with a nominal line width of 0.6 μm and aspect ratio up to 5:1, with accuracy within a few nanometers comparable to SEM results using the same sample.

Conference Presentation

Citation Download Citation

Zih-Ying Fu, Wei-Hsin Chein, Fu-Sheng Yang, and Liang-Chia Chen "Artificial-neural-network-assisted DUV scatterometry for OCD on HAR sub-micron structures", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124960E (27 April 2023); https://doi.org/10.1117/12.2657642

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