公开(公告)号：US20220342316A1

公开(公告)日：2022-10-27

申请号：US17640792

申请日：2020-09-03

Applicant: ASML Netherlands B.V.

Inventor： Marleen KOOIMAN ,  Maxim PISARENCO ,  Abraham SLACHTER ,  Mark John MASLOW ,  Bernardo Andres OYARZUN RIVERA ,  Wim Tjibbo TEL ,  Ruben Cornelis MAAS

IPC: G03F7/20 ,  G06T7/00 ,  G06T7/33

Abstract: Described herein is a method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate and determining etch conditions based on the trained model. The method includes obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location; and training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image. In an embodiment, the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.

公开(公告)号：US20230081821A1

公开(公告)日：2023-03-16

申请号：US17986829

申请日：2022-11-14

Applicant: ASML Netherlands B.V.

Inventor： Chrysostomos BATISTAKIS ,  Maxim PISARENCO ,  Bernardo Andres OYARZUN RIVERA ,  Abraham SLACHTER

IPC: G06T7/73 ,  G06V10/46 ,  G06T7/00 ,  H01L21/66

Abstract: Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.