公开(公告)号：EP3944020A1

公开(公告)日：2022-01-26

申请号：EP20186710.8

申请日：2020-07-20

申请人： ASML Netherlands B.V.

发明人： KHEDEKAR, Satej, Subhash ,  GANTAPARA, Anjan Prasad ,  CASTELIJNS, Henricus, Jozef ,  BOND, Stephen Henry ,  MOSSAVAT, Seyed Iman ,  YPMA, Alexander ,  DICKER, Gerald ,  STEINMEIER, Ewout, Klaas ,  VAN BERKEL, Koos ,  BOLDER, Joost, Johan ,  GUO, Chaoqun ,  LIN, Chenxi ,  CHEN, Hongwei ,  LI, Zhaoze ,  ZOU, Yi ,  ZHANG, Youping

IPC分类号： G03F7/20

摘要： 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 control output comprises an adjustment of the one or more parameters. 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.

公开(公告)号：EP4209846A1

公开(公告)日：2023-07-12

申请号：EP22150693.4

申请日：2022-01-10

申请人： ASML Netherlands B.V.

发明人： KOULIERAKIS, Eleftherios ,  GANTAPARA, Anjan Prasad ,  KHEDEKAR, Satej, Subhash ,  ROSTAMI, Hamideh

IPC分类号： G05B23/02 ,  G06N3/04 ,  G06N20/00

摘要： A computer-implemented method for training a diagnostic model for diagnosing a production system, wherein the production system comprises a plurality of sub-systems. The adaptive diagnostic model comprises, for each sub-system, a corresponding first learning model arranged to receive input data, and to generate compressed data for the production system in a corresponding compressed latent space. A second learning model is arranged to receive the compressed data generated by the first learning models, and generate further compressed data for the production system in a further compressed latent space. The method comprises performing unsupervised training of the first and second learning models based on training data derived from sensor data characterizing the sub-systems.