公开(公告)号：US20250060679A1

公开(公告)日：2025-02-20

申请号：US18701570

申请日：2022-10-17

Applicant: ASML NETHERLANDS B.V.

Inventor： Davide BARBIERI ,  Pascal CERFONTAINE

IPC: G03F7/00

Abstract: Autoencoder models may be used in the field of lithography to estimate, infer or predict a parameter of interest (e.g., metrology metrics). An autoencoder model is trained to predict a parameter by training it with measurement data (e.g., pupil images) of a substrate obtained from a measurement tool (e.g., optical metrology tool). Disclosed are methods and systems for synchronizing two or more autoencoder models for in-device metrology. Synchronizing two autoencoder models may configure the encoders of both autoencoder models to map from different signal spaces (e.g., measurement data obtained from different machines) to the same latent space, and the decoders to map from the same latent space to each autoencoder's respective signal space. Synchronizing may be performed for various purposes, including matching a measurement performance of one tool with another tool, and configuring a model to adapt to measurement process changes (e.g., changes in characteristics of the tool) over time.

公开(公告)号：US20240354552A1

公开(公告)日：2024-10-24

申请号：US18259344

申请日：2021-12-20

Applicant: ASML Netherlands B.V.

Inventor： Alexandru ONOSE ,  Bart Jacobus Martinus TIEMERSMA ,  Nick VERHEUL ,  Remco DIRKS ,  Davide BARBIERI ,  Hendrik Adriaan VAN LAARHOVEN

IPC: G06N3/0455

CPC classification number: G06N3/0455

Abstract: A modular autoencoder model is described. The modular autoencoder model comprises input models configured to process one or more inputs to a first level of dimensionality suitable for combination with other inputs: a common model configured to: reduce a dimensionality of combined processed inputs to generate low dimensional data in a latent space; and expand the low dimensional data in the latent space into one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; output models configured to use the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs; and a prediction model configured to estimate one or more parameters based on the low dimensional data in the latent space.