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22 February 2021 Data fusion by artificial neural network for hybrid metrology development
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Conference Poster
Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to improve metrology at nanoscale characterization by hybridization of several techniques. Namely, optical critical dimension (OCD) or scatterometry, CD–Scanning electron microscopy (CDSEM), CD–Atomic force microscopy (CDAFM) and CD–Small angle x-rays scattering (CDSAXS). With virtual data of tabular–type generated by modelling, the ANN is able to predict the geometrical parameters compared to true measured values with high accuracies and detect irregularities in input data.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
L. Penlap Woguia, J. Reche, M. Besacier, P. Gergaud, and G. Rademaker "Data fusion by artificial neural network for hybrid metrology development", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116112L (22 February 2021);

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