公开(公告)号：US20240111221A1

公开(公告)日：2024-04-04

申请号：US18275663

申请日：2022-01-12

Applicant: ASML NETHERLANDS B.V.

Inventor： Hendrik Adriaan VAN LAARHOVEN ,  Bastiaan Maurice VAN DEN BROEK ,  Vito Daniele RUTIGLIANI

IPC: G03F7/00

CPC classification number: G03F7/706841 ,  G03F7/70633 ,  G03F7/706831

Abstract: A method of determining a measurement recipe for measurement of in-die targets located within one or more die areas of an exposure field. The method includes obtaining first measurement data relating to measurement of a plurality of reference targets and second measurement data relating to measurement of a plurality of in-die targets, the targets having respective different overlay biases and measured using a plurality of different acquisition settings for acquiring the measurement data. One or more machine learning models are trained using the first measurement data to obtain a plurality of candidate measurement recipes, wherein the candidate measurement recipes include a plurality of combinations of a trained machine learned model and a corresponding acquisition setting; and a preferred measurement recipe is determined from the candidate measurement recipes using the second measurement data.

公开(公告)号：US20240369944A1

公开(公告)日：2024-11-07

申请号：US18287166

申请日：2022-04-12

Applicant: ASML NETHERLANDS B.V.

Inventor： Chrysostomos BATISTAKIS ,  Maxim PISARENCO ,  Markus Gerardus Martinus Maria VAN KRAAIJ ,  Vito Daniele RUTIGLIANI ,  Scott Anderson MIDDLEBROOKS ,  Coen Adrianus VERSCHUREN ,  Niels GEYPEN

IPC: G03F7/00 ,  G06N20/00

Abstract: A method of determining a stochastic metric, the method including: obtaining a trained model having been trained to correlate training optical metrology data to training stochastic metric data, wherein the training optical metrology data includes a plurality of measurement signals relating to distributions of an intensity related parameter across a zero or higher order of diffraction of radiation scattered from a plurality of training structures, and the training stochastic metric data includes stochastic metric values relating to the plurality of training structures, wherein the plurality of training structures have been formed with a variation in one or more dimensions on which the stochastic metric is dependent; obtaining optical metrology data including a distribution of the intensity related parameter across a zero or higher order of diffraction of radiation scattered from a structure; and using the trained model to infer a value of the stochastic metric from the optical metrology data.