公开(公告)号：EP4020085A1

公开(公告)日：2022-06-29

申请号：EP20216767.2

申请日：2020-12-22

Applicant: ASML Netherlands B.V.

Inventor： PISARENCO, Maxim ,  BATISTAKIS, Chrysostomos ,  MIDDLEBROOKS, Scott Anderson

IPC: G03F7/20 ,  G06N3/08

Abstract: A method for training a machine learning model includes obtaining a set of unpaired after-development images (AD) and after-etch (AE) images associated with a substrate. Each AD image in the set is obtained at a location on the substrate that is different from the location at which any of the AE images is obtained. The method further includes training the machine learning model to generate a predicted AE image based on the AD images and the AE images, wherein the predicted AE image corresponds to a location from which an input AD image of the AD images is obtained, and corresponding non-transitory computer-readable medium.

公开(公告)号：EP4231096A1

公开(公告)日：2023-08-23

申请号：EP22157745.5

申请日：2022-02-21

Applicant: ASML Netherlands B.V.

Inventor： BATISTAKIS, Chrysostomos ,  ZHANG, Huaichen

IPC: G03F7/20 ,  G06N3/08 ,  G06T7/00

Abstract: Disclosed is a method for determining a parameter of interest relating to at least one structure formed on a substrate in a manufacturing process. The method comprises obtaining metrology data relating to a plurality of measurements of the parameter of interest at a respective plurality of measurement locations on the substrate and layout data relating to a layout of a pattern to be applied to said structure, said pattern comprising said at least one structure. The method comprises determining a value for a parameter of interest at one or more locations on the substrate different from said measurement locations from said metrology data and layout data using a trained model, having been trained to be able to interpolate said metrology data using said layout data to an expected value for the parameter of interest.

公开(公告)号：EP4086703A1

公开(公告)日：2022-11-09

申请号：EP21172589.0

申请日：2021-05-06

Applicant: ASML Netherlands B.V.

Inventor： BATISTAKIS, Chrysostomos ,  PISARENCO, Maxim ,  VAN KRAAIJ, Markus Gerardus Martinus Maria ,  RUTIGLIANI, Vito Daniele ,  MIDDLEBROOKS, Scott Anderson

IPC: G03F7/20

Abstract: Disclosed is a method of determining at least one stochastic metric relating to a lithographic process and associated optical metrology device. The method comprises obtaining a trained machine learning model, the machine learning model having been trained to infer one or more stochastic metric values for said stochastic metric from optical metrology data. Optical metrology data comprising at least one measurement signal relating to a structure having been exposed in a lithographic process is obtained and the trained machine learning model used to infer a value for said stochastic metric from said optical metrology data.

公开(公告)号：EP3686674A1

公开(公告)日：2020-07-29

申请号：EP19154047.5

申请日：2019-01-28

Applicant: ASML Netherlands B.V.

Inventor： BATISTAKIS, Chrysostomos ,  DILLEN, Hermanus Adrianus ,  MAAS, Ruben Cornelis ,  VAN KRAAIJ, Markus Gerardus Martinus Maria

IPC: G03F7/20 ,  G03F7/38

Abstract: A lithographic method comprises providing a patterning device being capable of imparting a radiation beam with a pattern in its cross-section to form a patterned radiation beam; projecting by the radiation beam the pattern from the patterning device onto a substrate to provide a patterned substrate, and flooding the patterned substrate by an electron beam.
An electron beam flooding tool is arranged to flood a patterned substrate by an electron beam. The electron beam flooding tool may be arranged in line in a lithographic system.