Asml Netherlands B.v.

Leiwu Zheng

Yen-Wen Lu

Feng Chen

Jen-Shiang Wang

Yongfa Fan

Mu Feng

Rafael C Howell

Xi Chen

Qian Zhao

Ya Luo

Yu Zhao

Jin Cheng

Mir Farrokh Shayegan Salek

Yi-Yin Chen

Dianwen Zhu

Ziyang Ma

Xin Guo

Chenji Zhang

A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

Method for training or using a process model for determining a pattern in a patterning process

A method involving determining an etch bias for a pattern to be etched using an etch step of a patterning process based on an etch bias model, the etch bias model including a formula having a variable associated with a spatial property of the pattern or with an etch plasma species concentration of the etch step, and including a mathematical term including a natural exponential function to the power of a parameter that is fitted or based on an etch time of the etch step; and adjusting the patterning process based on the determined etch bias.

Etch bias characterization and method of using the same

Methods of constructing a process model for simulating a characteristic of a product of lithography from patterns produced under different processing conditions. The methods use a deviation between the variation of the simulated characteristic and the variation of the measured characteristic to adjust a parameter of the process model.

Methods of tuning process models

Growing community of inventors

San Jose, CA, United States of America

Leiwu Zheng