公开(公告)号：WO2019224176A1

公开(公告)日：2019-11-28

申请号：PCT/EP2019/063053

申请日：2019-05-21

Applicant: ASML NETHERLANDS B.V.

Inventor： LI, Danying ,  FAN, Chi-Hsiang ,  ELMALK, Abdalmohsen ,  ZHANG, Youping ,  CHEN, Jay, Jianhui ,  HUANG, Kui-Jun

IPC: G03F7/20

Abstract: Described herein is are method for determining a stack configuration for a substrate subjected a patterning process. The method includes obtaining (i) measurement data of a stack configuration with location information on a printed substrate, (ii) a substrate model configured to predict a stack characteristic based on a location of the substrate, and (iii) a stack map including a plurality of stack configurations based on the substrate model. The method iteratively determines values of model parameters of the substrate model based on a fitting between the measurement data and the plurality of stack configurations of the stack map; and predicts an optimum stack configuration at a particular location based on the substrate model using the values of the model parameters.

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.