公开(公告)号：WO2013092106A1

公开(公告)日：2013-06-27

申请号：PCT/EP2012/073396

申请日：2012-11-22

Applicant: ASML NETHERLANDS B.V.

Inventor： ELINGS, Wouter ,  VAN BILSEN, Franciscus ,  DE MOL, Christianus ,  MOS, Everhardus ,  TOLSMA, Hoite ,  TEN BERGE, Peter ,  VAN WIJNEN, Paul ,  VERSTAPPEN, Leonardus ,  DICKER, Gerald ,  JUNGBLUT, Reiner ,  CHUNG-HSUN, Li

IPC: G01N21/956

CPC classification number: G01B11/002 ,  G01B11/02 ,  G01B11/14 ,  G01B11/26 ,  G01N21/9501 ,  G01N21/956 ,  G01N21/95607 ,  G03F7/70508 ,  G03F7/70625 ,  G03F7/70633 ,  G05B19/41875 ,  G05B2219/37224 ,  H01L22/20 ,  Y02P90/20 ,  Y02P90/22

Abstract: In the measurement of properties of a wafer substrate, such as Critical Dimension or overlay a sampling plan is produced (2506) defined for measuring a property of a substrate, wherein the sampling plan comprises a plurality of sub-sampling plans. The sampling plan may be constrained to a predetermined fixed number of measurement points and is used (2508) to control an inspection apparatus to perform a plurality of measurements of the property of a plurality of substrates using different sub-sampling plans for respective substrates, optionally, the results are stacked (2510) to at least partially recompose the measurement results according to the sample plan.

Abstract translation: 在测量晶片衬底的特性（例如临界尺寸或覆盖层）时，产生用于测量衬底性质的采样计划（2506），其中采样计划包括多个次采样图。 采样计划可以被约束到预定的固定数量的测量点，并且被使用（2508）来控制检查装置，以使用对于各个基板的不同的子采样计划来执行多个基板的属性的多个测量 ，结果（2510）根据样本计划至少部分地重新组合测量结果。

公开(公告)号：WO2022008174A1

公开(公告)日：2022-01-13

申请号：PCT/EP2021/065947

申请日：2021-06-14

Applicant: ASML NETHERLANDS B.V.

Inventor： GUO, Chaoqun ,  KHEDEKAR, Satej, Subhash ,  GANTAPARA, Anjan Prasad ,  LIN, Chenxi ,  CASTELIJNS, Henricus, Jozef ,  CHEN, Hongwei ,  BOND, Stephen Henry ,  LI, Zhaoze ,  MOSSAVAT, Seyed Iman ,  ZOU, Yi ,  YPMA, Alexander ,  ZHANG, Youping ,  DICKER, Gerald ,  STEINMEIER, Ewout, Klaas ,  VAN BERKEL, Koos ,  BOLDER, Joost, Johan ,  HUBAUX, Arnaud ,  HLOD, Andriy, Vasyliovich ,  GONZALEZ HUESCA, Juan Manuel ,  AARDEN, Frans Bernard

IPC: G03F7/20

Abstract: Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input comprises one or more parameters used in the patterning process. The control output is generated with a trained machine learning model based on the control input. The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data. The training data comprises 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.