Processing

Please wait...

PATENTSCOPE will be unavailable a few hours for maintenance reason on Tuesday 25.01.2022 at 9:00 AM CET
Settings

Settings

Goto Application

1. WO2022008198 - MOTION CONTROL USING AN ARTIFICIAL NEURAL NETWORK

Publication Number WO/2022/008198
Publication Date 13.01.2022
International Application No. PCT/EP2021/066479
International Filing Date 17.06.2021
IPC
G03F 7/20 2006.1
GPHYSICS
03PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
FPHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
7Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printed surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
20Exposure; Apparatus therefor
G05B 13/02 2006.1
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
G05B 13/04 2006.1
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
04involving the use of models or simulators
G06N 3/02 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
G06N 3/08 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 20/00 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
Applicants
  • ASML NETHERLANDS B.V. [NL]/[NL]
Inventors
  • VAN BERKEL, Koos
  • BOLDER, Joost, Johan
  • BOSMA, Stijn
Agents
  • ASML NETHERLANDS B.V.
Priority Data
63/049,71909.07.2020US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) MOTION CONTROL USING AN ARTIFICIAL NEURAL NETWORK
(FR) COMMANDE DE MOUVEMENT À L'AIDE D'UN RÉSEAU NEURONAL ARTIFICIEL
Abstract
(EN) 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 (ST) of an apparatus with at least one prescribed movement. The processor is configured to receive a control input (SP) 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 (PM), based on the control input (SP), a control output for the component (ST). The artificial neural network is trained with training data such that the artificial neural network determines the control output regardless of whether or not the control input falls outside the training data. The processor controls the component based on at least the control output.
(FR) Des points de consigne variables et/ou d'autres facteurs peuvent limiter la commande par apprentissage itératif visant à déplacer des composants d'un appareil. La présente invention concerne un processeur conçu pour commander le mouvement d'un composant (ST) d'un appareil ayant au moins un mouvement prescrit. Le processeur est conçu pour recevoir une entrée de commande (SP) qui peut être une valeur de consigne variable ou qui comprend une valeur de consigne variable. L'entrée de commande indique au moins ledit mouvement prescrit pour le composant. Le processeur est conçu pour déterminer, avec un réseau neuronal artificiel formé (PM) et sur la base de l'entrée de commande (SP), une sortie de commande pour le composant (ST). Le réseau neuronal artificiel est formé avec des données d'apprentissage de telle sorte que le réseau neuronal artificiel détermine la sortie de commande indépendamment de si l'entrée de commande se trouve en dehors des données d'apprentissage ou non. Le processeur commande le composant au moins sur la base de la sortie de commande.
Latest bibliographic data on file with the International Bureau