公开(公告)号：US20210405521A1

公开(公告)日：2021-12-30

申请号：US17180984

申请日：2021-02-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Taehoon Kim ,  Jaeho Jeong ,  Jeonghoon Ko ,  Jongwon Kim ,  Yejin Jeong ,  Changwook Jeong

IPC: G03F1/36 ,  G03F7/20 ,  H01L21/027

Abstract: A proximity correction method for a semiconductor manufacturing process includes: generating a plurality of pieces of original image data from a plurality of sample regions, with the sample regions selected from layout data used in the semiconductor manufacturing process; removing some pieces of original image data that overlap with each other from the plurality of pieces of original image data, resulting in a plurality of pieces of input image data; inputting the plurality of pieces of input image data to a machine learning model; obtaining a prediction value of critical dimensions of target patterns included in the plurality of pieces of input image data from the machine learning model; measuring a result value for critical dimensions of actual patterns corresponding to the target patterns on a semiconductor substrate on which the semiconductor manufacturing process is performed; and performing learning of the machine learning model using the prediction value and the result value.

公开(公告)号：US11733603B2

公开(公告)日：2023-08-22

申请号：US17180984

申请日：2021-02-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Taehoon Kim ,  Jaeho Jeong ,  Jeonghoon Ko ,  Jongwon Kim ,  Yejin Jeong ,  Changwook Jeong

IPC: G03F1/36 ,  H01L21/027 ,  G03F7/20 ,  G03F7/00

CPC classification number: G03F1/36 ,  G03F7/70441 ,  G03F7/70625 ,  H01L21/027

Abstract: A proximity correction method for a semiconductor manufacturing process includes: generating a plurality of pieces of original image data from a plurality of sample regions, with the sample regions selected from layout data used in the semiconductor manufacturing process; removing some pieces of original image data that overlap with each other from the plurality of pieces of original image data, resulting in a plurality of pieces of input image data; inputting the plurality of pieces of input image data to a machine learning model; obtaining a prediction value of critical dimensions of target patterns included in the plurality of pieces of input image data from the machine learning model; measuring a result value for critical dimensions of actual patterns corresponding to the target patterns on a semiconductor substrate on which the semiconductor manufacturing process is performed; and performing learning of the machine learning model using the prediction value and the result value.