公开(公告)号：US20240086599A1

公开(公告)日：2024-03-14

申请号：US18509654

申请日：2023-11-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Hyunjoong Kim ,  Jaepil Shin ,  Moonhyun Cha ,  Changwook Jeong

IPC: G06F30/27 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06F30/27 ,  G06N3/045 ,  G06N3/08 ,  G06F2119/02

Abstract: A system for modeling a semiconductor fabrication process includes at least one first processor and at least one second processor. The at least one first processor is configured to provide at least one machine learning (ML) model, which is trained by using a plurality of pairs of images of a design pattern sample and a physical pattern sample. The physical pattern sample is formed from the design pattern sample by using the semiconductor fabrication process. The at least one second processor is configured to provide an input image representing a shape of a design pattern and/or a physical pattern to the at least one first processor and to generate output data defining the physical pattern and/or the design pattern based on an output image received from the at least one first processor.

公开(公告)号：US20220092239A1

公开(公告)日：2022-03-24

申请号：US17231428

申请日：2021-04-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Hyunjoong Kim ,  Jaepil Shin ,  Moonhyun Cha ,  Changwook Jeong

IPC: G06F30/27 ,  G06N3/08 ,  G06N3/04

Abstract: A system for modeling a semiconductor fabrication process includes at least one first processor and at least one second processor. The at least one first processor is configured to provide at least one machine learning (ML) model, which is trained by using a plurality of pairs of images of a design pattern sample and a physical pattern sample. The physical pattern sample is formed from the design pattern sample by using the semiconductor fabrication process. The at least one second processor is configured to provide an input image representing a shape of a design pattern and/or a physical pattern to the at least one first processor and to generate output data defining the physical pattern and/or the design pattern based on an output image received from the at least one first processor.

公开(公告)号：US11853660B2

公开(公告)日：2023-12-26

申请号：US17231428

申请日：2021-04-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Hyunjoong Kim ,  Jaepil Shin ,  Moonhyun Cha ,  Changwook Jeong

IPC: H01L21/027 ,  G06F30/39 ,  G06F30/27 ,  G06N3/08 ,  G06N3/045 ,  G06F119/02 ,  G06F119/18

CPC classification number: G06F30/27 ,  G06N3/045 ,  G06N3/08 ,  G06F2119/02 ,  G06F2119/18

Abstract: A system for modeling a semiconductor fabrication process includes at least one first processor and at least one second processor. The at least one first processor is configured to provide at least one machine learning (ML) model, which is trained by using a plurality of pairs of images of a design pattern sample and a physical pattern sample. The physical pattern sample is formed from the design pattern sample by using the semiconductor fabrication process. The at least one second processor is configured to provide an input image representing a shape of a design pattern and/or a physical pattern to the at least one first processor and to generate output data defining the physical pattern and/or the design pattern based on an output image received from the at least one first processor.

公开(公告)号：US20240320402A1

公开(公告)日：2024-09-26

申请号：US18544065

申请日：2023-12-18

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sangjune Bae ,  Taeyoon An ,  Joohyung You ,  In Huh ,  Moonhyun Cha ,  Jaemyung Choe

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: Provided are a method, a device, and a system for estimating threshold values of a kernel density function with respect to defects of a product. The method includes a bootstrapping sampling operation, estimating optimal kernel bandwidths for sample data sets by using a bandwidth estimation method selected according to a number of sample data from among a plurality of bandwidth estimation methods, estimating threshold values corresponding to a tail region of the kernel density function based on the optimal kernel bandwidths, and providing a quantitative value for quantifying uncertainty of the threshold values based on the plurality of threshold values.