Mr. Yuta Kawamoto Profile

Yuta Kawamoto

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Proceedings Article | 22 February 2021 Presentation + Paper

Multivariate analysis methodology for the study of massive multidimensional SEM data

Mohamed Saib, Gian Francesco Lorusso, Anne-Laure Charley, Philippe Leray, Tsuyoshi Kondo, Yuta Kawamoto, Yasushi Ebizuka, Naoma Ban

Proceedings Volume 11611, 116112C (2021) https://doi.org/10.1117/12.2583696

KEYWORDS: Semiconducting wafers, Scanning electron microscopy, Metrology, Finite element methods, Extreme ultraviolet lithography, Data analysis, Structural design, Semiconductors, Process control, Photoresist processing

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Over the years, the reduction in the size of semiconductor devices has made their performances extremely sensitive to small differences between printed structures and intended design. As a consequence, metrology equipment manufacturers are nowadays proposing new tool configurations, able to ensure quality control in such a challenging environment by generating massive multi-properties measurement sets from inspected wafers. However, the unprecedented amount of acquired measurements and their intrinsic diversity creates a new challenge in terms of data analysis. In this work, we propose an analysis method suitable for massive multi-descriptors data sets and apply it to the processing of measurements acquired on the GS1000, the latest generation e-beam metrology tool from Hitachi. This new approach is based on the Parallel Coordinates Plot (PCP). The PCP representation is very efficient to condensate multidimensional data into a single plot, but not adapted to large data sets due to over-plotting problems. To overcome these issues, we have developed specific strategies to enable PCP to be efficient on massive data analysis by both extracting neighbors' properties by median depiction and the multi-properties dispersion. The experimental validation has been carried out over 1.7 billion Contact Hole (CH) measurements acquired on a test wafer. 28 different properties have been quantified from the e-beam images for each pattern and grouped into 3 categories: size area, edge placement error, and gap. The analysis of the full data set with the proposed methodology clearly showed the FEM fingerprint and allowed us to determine the process window based on the multi-criteria analysis. By combining the PCP with an Artificial Neural Network (ANN) we were able to model accurately the stochastic cliffs defects' density.

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