Machine learning ILT for memory customers

Thomas Cecil; Kyle Braam; Ahmed Omran; Amyn Poonawala; Jason Shu; Clark Vandam

doi:10.1117/12.2587107

22 March 2021 Machine learning ILT for memory customers

Thomas Cecil, Kyle Braam, Ahmed Omran, Amyn Poonawala, Jason Shu, Clark Vandam

Proceedings Volume 11613, Optical Microlithography XXXIV; 116130P (2021) https://doi.org/10.1117/12.2587107
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

In this paper, we will present a machine learning solution targeted for memory customers including both assist feature and main feature mask synthesis. In a previous paper, we demonstrated machine learning ILT solutions for the creation of assist features using a neural network. In this paper, we extend the solution to include main features masks, which we can create using machine learning models which take into account the full ILT corrected masks during training. In practice, while the correction of main features is often visually more intuitive, there are underlying edge to edge and polygon to polygon interactions that are not easily captured by local influence edge perturbations found in typical OPC solvers but can be captured by ILT and machine learning solutions trained on ILT masks.

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Thomas Cecil, Kyle Braam, Ahmed Omran, Amyn Poonawala, Jason Shu, and Clark Vandam "Machine learning ILT for memory customers", Proc. SPIE 11613, Optical Microlithography XXXIV, 116130P (22 March 2021); https://doi.org/10.1117/12.2587107

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KEYWORDS

Machine learning

Neural networks

Optical proximity correction

Visualization

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