Data fusion by artificial neural network for hybrid metrology development
| Autor: | G. Rademaker, L. Penlap Woguia, Patrice Gergaud, Maxime Besacier, Jérôme Reche |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Computer Science::Neural and Evolutionary Computation Pattern recognition Sensor fusion Metrology Characterization (materials science) Microscopy Line (geometry) Development (differential geometry) Artificial intelligence business Critical dimension |
| Zdroj: | Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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