公开(公告)号：US20230315027A1

公开(公告)日：2023-10-05

申请号：US18013154

申请日：2021-06-17

申请人： ASML NETHERLANDS B.V.

发明人： Koos VAN BERKEL ,  Joost Johan BOLDER ,  Stijn BOSMA

IPC分类号： G05B13/02 ,  G06N3/084 ,  G03F7/00

CPC分类号： G05B13/027 ,  G03F7/706841 ,  G06N3/084

摘要： Variable setpoints and/or other factors may limit iterative learning control for moving components of an apparatus. The present disclosure describes a processor configured to control movement of a component of an apparatus with at least one prescribed movement. The processor is configured to receive a control input such as and/or including a variable setpoint. The control input indicates the at least one prescribed movement for the component. The processor is configured to determine, with a trained artificial neural network, based on the control input, a feedforward output for the component. The artificial neural network is pretrained with a training data set such that the artificial neural network determines the output regardless of whether or not the control input falls outside the training data set. The processor controls the component based on at least the output.

公开(公告)号：US20200326636A1

公开(公告)日：2020-10-15

申请号：US16756629

申请日：2018-09-05

申请人： ASML Netherlands B.V.

发明人： Joost Johan BOLDER ,  Peter Michel Silvester Maria HEIJMANS ,  Jeroen VAN DUIVENBODE ,  Ruud Hubertus Silvester VRENKEN

IPC分类号： G03F7/20 ,  H02P25/064 ,  H02P6/00 ,  H02P6/182 ,  H02K41/03

摘要： The invention relates to a motor (LD) comprising: a stationary part (STP), comprising: a row of coil assemblies (UCA,LCA), the coil assemblies having multiple phases, a movable part (MP), comprising: a row of permanent magnets (UPM,LPM), wherein the row of coil assemblies has a first length and the row of permanent magnets has a second length, wherein the second length is smaller than the first length, wherein the coil assemblies are arranged to interact with permanent magnets aligned with the coil assemblies to generate a driving force, a comparator to compare a position measurement signal representative for an actual position of the movable part with a set-point signal representative for a desired position of the movable part to provide an error signal; a motion feedback controller configured to provide a control signal on the basis of the error signal; at least one current amplifier configured to provide an actuation signal to the coil assemblies on the basis of the control signal, wherein the motor comprises a feedforward device, wherein the feedforward device is configured to provide a current amplifier feedforward signal on the basis of the set-point signal, or a derivative thereof, wherein the current amplifier feedforward signal is provided to the at least one current amplifier to compensate for unbalanced back electromotive forces on one or more of the coil assemblies due to the one or more coil assemblies being only partly aligned with the permanent magnets.

公开(公告)号：US20230273529A1

公开(公告)日：2023-08-31

申请号：US18012222

申请日：2021-06-14

申请人： ASML NETHERLANDS B.V.

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

IPC分类号： G03F7/20

CPC分类号： G03F7/70525 ,  G03F7/70633 ,  G03F7/7065

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 includes one or more parameters used in the patterning process. The control output is generated with a trained machine learning mod& 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 includes 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.