公开(公告)号：US20240377343A1

公开(公告)日：2024-11-14

申请号：US18684558

申请日：2022-08-22

Applicant: ASML NETHERLANDS B.V.

Inventor： Yunbo GUO ,  Zhu WANG ,  Feng YANG ,  Qian ZHAO ,  Mu FENG ,  Jen-Shiang WANG

IPC: G01N23/2251 ,  G06N3/045 ,  G06N3/0475 ,  G06N3/094

Abstract: Described herein is a metrology system and a method for converting metrology data via a trained machine learning (ML) model. The method includes accessing a first (MD1) SEM data set (e.g., images, contours, etc.) acquired by a first scanning electron metrology (SEM) system (TS1) and a second (MD2) SEM data set acquired by a second SEM system (TS2), where the first SEM data set and the second SEM data set being associated with a patterned substrate. Using the first SEM data set and the second SEM data set as training data, a machine learning (ML) model is trained (P303) such that the trained ML model is configured to convert (P307) a metrology data set (310) acquired (P305) by the second SEM system to a converted data set (311) having characteristics comparable to metrology data being acquired by the first SEM system. Furthermore, measurements may be determined based on the converted SEM data.

公开(公告)号：US20240345487A1

公开(公告)日：2024-10-17

申请号：US18681613

申请日：2022-08-02

Applicant: ASML NETHERLANDS B.V.

Inventor： Rencheng SUN ,  Feng YANG ,  Meng LIU ,  Fei YAN

IPC: G03F7/00

CPC classification number: G03F7/705 ,  G03F7/70441

Abstract: Systems and methods for evaluating selected set of patterns of a design layout. A method herein includes obtaining (i) a first pattern set resulting from a pattern selection process, (ii) first pattern data associated with the first pattern set, (iii) characteristic data associated with the first pattern data, and (iv) second pattern data associated with a second pattern set. A machine learning model is trained based on the characteristic data, where the machine learning model being configured to predict pattern data for an input pattern. The second pattern set is input to the trained machine learning model to predict second pattern data of the second pattern set. The first pattern set is evaluated by comparing the second pattern data and the predicted second pattern data. If the evaluation indicates insufficient pattern coverage, additional patterns can be included to improve the pattern coverage.