Vol. 3 No. 1 (2023): Issue 3
Articles

Lithography Simulation Acceleration through Gradient Boosting Machines

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  • Tanaka Sakura,
  • Nakamura Hiroshi

Published 2023-06-29

Keywords

  • Lithography Technology,
  • Simulation Tools,
  • Machine Learning,
  • Process Optimization,
  • Research Advancement

How to Cite

Sakura, T., & Hiroshi, N. (2023). Lithography Simulation Acceleration through Gradient Boosting Machines. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/37
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

The rapid advancement of lithography technology in the semiconductor industry has driven the need for efficient simulation tools to predict and optimize the manufacturing process. However, the complexity and computational demand of lithography simulations pose significant challenges to current research efforts. Traditional simulation methods often suffer from long processing times and limited accuracy, hindering the rapid iteration required for process improvement. In response to these challenges, this paper proposes a novel approach utilizing Gradient Boosting Machines to accelerate lithography simulations. By harnessing machine learning techniques, our method offers a more efficient and accurate solution for lithography simulation, enabling faster and more precise optimization of manufacturing processes. This research contributes to the advancement of lithography technology by introducing a new paradigm for simulation acceleration, bridging the gap between traditional methods and the demands of modern semiconductor manufacturing.

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