公开(公告)号：WO2022135819A1

公开(公告)日：2022-06-30

申请号：PCT/EP2021/082886

申请日：2021-11-24

Applicant: ASML NETHERLANDS B.V.

Inventor： LI, Danying ,  LIU, Meng ,  WUU, Jen-Yi ,  SUN, Rencheng ,  WU, Cong ,  XU, Dean

IPC: G03F7/20 ,  G06F30/398

Abstract: An improved apparatus and method of feature extraction for identifying a pattern are disclosed. An improved method of feature extraction for identifying a pattern comprises obtaining data representative of a pattern instance, dividing the pattern instance into a plurality of zones, determining a representative characteristic of a zone of the plurality of zones, generating a representation of the pattern instance using a feature vector, wherein the feature vector comprises an element corresponding to the representative characteristic, wherein the representative characteristic is indicative of a spatial distribution of one or more features of the zone. The method also comprises at least one of classifying or selecting pattern instances based on the feature vector.

公开(公告)号：WO2020135988A1

公开(公告)日：2020-07-02

申请号：PCT/EP2019/083585

申请日：2019-12-04

Applicant: ASML NETHERLANDS B.V.

Inventor： ZHANG, Youping ,  GENIN, Maxime, Philippe, Frederic ,  WU, Cong ,  SU, Jing ,  HU, Weixuan ,  ZOU, Yi

IPC: G03F7/20

Abstract: Described herein are methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method of training a machine learning model of a patterning process involves obtaining a training data set comprising: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; training the machine learning model based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model is further used for determining ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.