公开(公告)号：US20240061347A1

公开(公告)日：2024-02-22

申请号：US18259354

申请日：2021-12-20

Applicant: ASML Netherlands B.V.

Inventor： Alexandru ONOSE ,  Bart Jacobus Martinus TIEMERSMA ,  Nick VERHEUL ,  Remco DIRKS

IPC: G03F7/00 ,  G01N21/95 ,  G06N3/0455 ,  G06N3/08

CPC classification number: G03F7/706839 ,  G01N21/9501 ,  G03F7/70616 ,  G06N3/0455 ,  G06N3/08

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.

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.

公开(公告)号：US20240060906A1

公开(公告)日：2024-02-22

申请号：US18270074

申请日：2021-12-20

Applicant: ASML Netherlands B.V.

Inventor： Bart Jacobus Martinus TIEMERSMA ,  Alexandru ONOSE ,  Nick VERHEUL ,  Remco DIRKS

IPC: G01N21/95 ,  G03F7/00 ,  G06N3/0455 ,  G06N3/0895

CPC classification number: G01N21/9501 ,  G03F7/70625 ,  G03F7/706839 ,  G06N3/0455 ,  G06N3/0895

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.