Sunnyvale, CA, United States of America

Xin Guo


Average Co-Inventor Count = 1.0


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations of Xin Guo in Patterning Process Models

Introduction

Xin Guo is an accomplished inventor based in Sunnyvale, CA. He has made significant contributions to the field of patterning processes, particularly through his innovative patent. His work focuses on enhancing the accuracy and efficiency of patterning processes, which are crucial in various technological applications.

Latest Patents

Xin Guo holds a patent titled "Method for training or using a process model for determining a pattern in a patterning process." This patent describes a method for training a patterning process model that predicts the patterns formed during a patterning process. The method involves obtaining image data related to a desired pattern and a measured pattern of the substrate. It utilizes a first model with a set of parameters and a machine learning model with another set of parameters. The process iteratively determines the values of these parameters to train the patterning process model effectively. An iteration includes executing both models to predict a printed pattern and adjusting the parameters to minimize the difference between the measured and predicted patterns. Xin Guo has 1 patent to his name.

Career Highlights

Xin Guo is currently employed at ASML Netherlands B.V., a leading company in the field of lithography systems for the semiconductor industry. His role involves applying his expertise in machine learning and patterning processes to develop advanced technologies that drive innovation in semiconductor manufacturing.

Collaborations

Xin Guo collaborates with talented professionals in his field, including his coworker Ziyang Ma, who brings valuable insights and expertise to their projects.

Conclusion

Xin Guo's innovative work in patterning process models showcases his commitment to advancing technology in the semiconductor industry. His contributions are paving the way for more efficient and accurate manufacturing processes.

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