- Patent Title: Large scale computational lithography using machine learning models
-
Application No.: US17751549Application Date: 2022-05-23
-
Publication No.: US12249115B2Publication Date: 2025-03-11
- Inventor: Dereje Shewaseged Woldeamanual , Thomas Heribert Mülders , Jiuzhou Tang , Rainer Zimmermann , Robert Marshall Lugg , Hans-Jürgen Stock , Georg Albert Viehöver
- Applicant: Synopsys, Inc.
- Applicant Address: US CA Mountain View
- Assignee: Synopsys, Inc.
- Current Assignee: Synopsys, Inc.
- Current Assignee Address: US CA Mountain View
- Agency: Fenwick & West LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G03F7/00 ; G06N5/04 ; G06V10/46 ; G06V10/75

Abstract:
A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.
Public/Granted literature
- US20220392191A1 LARGE SCALE COMPUTATIONAL LITHOGRAPHY USING MACHINE LEARNING MODELS Public/Granted day:2022-12-08
Information query