Application of artificial neural networks to compact mask models in optical lithography simulation

Viviana Agudelo; Tim Fühner; Andreas Erdmann; Peter Evanschitzky

doi:10.1117/12.2011132

12 April 2013 Application of artificial neural networks to compact mask models in optical lithography simulation

Viviana Agudelo, Tim Fühner, Andreas Erdmann, Peter Evanschitzky

Proceedings Volume 8683, Optical Microlithography XXVI; 868317 (2013) https://doi.org/10.1117/12.2011132
Event: SPIE Advanced Lithography, 2013, San Jose, California, United States

Abstract

Compact mask models provide an alternative to speed up rigorous mask diffraction computation based on electromagnetic field (EMF) modeling. The high time expense of the rigorous mask models in the simulation process challenges the exploration of innovative modeling techniques to compromise accuracy and speed in the computation of the diffracted field and vectorial imaging in optical lithographic systems. The Artificial Neural Network (ANN) approach is presented as an alternative to retrieve the spectrum of the mask layout in an accurate yet efficient way. The validity of the ANN for different illuminations, feature sizes, pitches and shapes is investigated. The evaluation of the performance of this approach is performed by a process windows analysis, comparison of the spectra, best focus and critical dimension (CD) through pitch. The application of various layouts demonstrated that the ANN can also be trained with different patterns to reproduce various effects such as: shift of the line position, different linewidths and line ends. Comparisons of the ANN approach with other compact models such as boundary layer model, pulses modification, spectrum correction and pupil filtering techniques are presented.

Citation Download Citation

Viviana Agudelo, Tim Fühner, Andreas Erdmann, and Peter Evanschitzky "Application of artificial neural networks to compact mask models in optical lithography simulation", Proc. SPIE 8683, Optical Microlithography XXVI, 868317 (12 April 2013); https://doi.org/10.1117/12.2011132

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KEYWORDS

Photomasks

Diffraction

Artificial neural networks

Systems modeling

Lithography

Optical lithography

Performance modeling

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