Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

被引：4

作者：

Shi, Xuelong ^{[1
]}

Yan, Yan ^{[1
]}

Zhou, Tao ^{[1
]}

Yu, Xueru ^{[1
]}

Li, Chen ^{[1
]}

Chen, Shoumian ^{[1
]}

Zhao, Yuhang ^{[1
]}

机构：

[1] Shanghai Integrated Circuits R&D Ctr Co Ltd, Shanghai, Peoples R China

来源：

IWAPS 2020: PROCEEDINGS OF 2020 4TH INTERNATIONAL WORKSHOP ON ADVANCED PATTERNING SOLUTIONS (IWAPS) | 2020年

关键词：

Optimal feature maps; inverse lithography technology (ILT); deep convolution neural network (DCNN);

D O I：

10.1109/iwaps51164.2020.9286814

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To achieve full chip inverse lithography technology (ILT) solution, we proposed a hybrid approach in this study by combining first few physics based feature maps as model input with a specially designed DCNN structure to learn the rigorous ILT algorithm. Our results show that this approach can make machine learning ILT easy, fast and more accurate.

引用

页码：9 / 11

页数：3

共 50 条

[21] Fast Temporal Projection Using Accurate Physics-Based Geometric Reasoning
Moesenlechner, Lorenz
Beetz, Michael
2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2013, : 1821 - 1827
[22] Physics-based and machine-learning models for accurate scour depth prediction
Jatoliya, Ajay
Bhattacharya, Debayan
Manna, Bappaditya
Bento, Ana Margarida
Ferradosa, Tiago Fazeres
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2024, 382 (2264):
[23] A fast feature selection approach based on extreme learning machine and coefficient of variation
Ertugrul, Omer Faruk
Tagluk, Mehmet Emin
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2017, 25 (04) : 3409 - 3420
[24] Using machine learning in physics-based simulation of fire
Lattimer, B. Y.
Hodges, J. L.
Lattimer, A. M.
FIRE SAFETY JOURNAL, 2020, 114 (114)
[25] Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks
Tripathi, Gaurav
Singh, Vishal Krishna
Sharma, Varun
Vinodbhai, Majithia Vivek
IEEE ACCESS, 2024, 12 : 20973 - 20982
[26] Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis
Choi, Sanghyun
Xie, Qian
Greeneltch, Nathan
Lee, Hyung Joo
Govindaraj, Mohan
Jayaram, Srividya
Pereira, Mark
Biswas, Sayani
Bhamidipati, Samir
Torunoglu, Ilhami
METROLOGY, INSPECTION, AND PROCESS CONTROL XXXVIII, 2024, 12955
[27] Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple
Su, Tianhao
Cui, Yaning
Lian, Zhengheng
Hu, Minglang
Li, Minjie
Lu, Wencong
Ren, Wei
JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2021, 12 (35): : 8521 - 8527
[28] Feature Selection for Ranking using Heuristics based Learning to Rank using Machine Learning
Chavhan, Sushilkumar
Dharmik, R. C.
INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, 2022, 13 (05): : 976 - 983
[29] Accurate Characterization of Mixed Plastic Waste Using Machine Learning and Fast Infrared Spectroscopy
Zinchik, Stas
Jiang, Shengli
Friis, Soren
Long, Fei
Hogstedt, Lasse
Zavala, Victor M.
Bar-Ziv, Ezra
ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 2021, 9 (42) : 14143 - 14151
[30] A fast fuel cell parametric identification approach based on machine learning inverse models
Guarino, Antonio
Trinchero, Riccardo
Canavero, Flavio
Spagnuolo, Giovanni
ENERGY, 2022, 239

← 1 2 3 4 5 →