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Experimental identification of a grating profile using neural network classifiers in optical scatterometry
Applied Optics
|October 6, 2021
Summary
This study introduces a novel grating characterization technique using two artificial neural networks (ANNs). The method accurately identifies grating profiles and determines geometrical parameters for improved scatterometry applications.
Area of Science:
- Optics and Photonics
- Computational Science
Background:
- Grating characterization is crucial for optical systems.
- Existing scatterometry methods face challenges in precision and efficiency.
Purpose of the Study:
- To develop a novel, two-step grating characterization technique.
- To enhance the accuracy and efficiency of geometrical parameter determination for diffraction gratings.
Main Methods:
- Utilizing an artificial neural network (ANN) in classifier mode to identify grating profile shapes from optical signatures.
- Employing a second ANN in regression mode to determine precise geometrical parameters.
- Validating the approach using scatterometry error criteria.
Main Results:
- Successful identification of geometrical profiles and determination of parameters for 500 and 750 nm period gratings.
- Demonstrated the effectiveness of the ANN-based approach in grating characterization.
- Highlighted the advantages of the proposed method over conventional techniques.
Conclusions:
- The developed two-step ANN technique offers a robust and accurate method for grating characterization.
- This approach has significant potential for advancing scatterometry and related optical metrology fields.
