A Machine Learning-Based Solution for Modulated Image Analysis in Lithography Process Stability Diagnosis

被引：1

作者：

Wu, Peng ^{[1
]}

Zhu, Qi ^{[1
]}

Yang, Jihong ^{[1
]}

Sun, Changjie ^{[1
]}

Zhu, Yiming ^{[1
]}

Vikram, Abhishek ^{[2
,3
]}

Ye, Chen ^{[2
,3
]}

Cheng, Guojie ^{[2
,3
]}

Wang, Hui ^{[2
,3
]}

Zhang, Qing ^{[2
,3
]}

Liao, Wenkui ^{[2
,3
]}

机构：

[1] Shanghai Huali Integrated Circuit Corp, 6 Liangteng Rd, Shanghai 201314, Peoples R China

[2] KLA Corp, 668 East Beijing Rd,East Bldg 19B, Shanghai 200001, Peoples R China

[3] 1 Technol Dr, Milpitas, CA 95035 USA

来源：

DTCO AND COMPUTATIONAL PATTERNING | 2022年 / 12052卷

关键词：

Process stability diagnosis; focus exposure modulation; SEM images; pattern centric; machine learning; process window analysis;

D O I：

10.1117/12.2613403

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Automated image analysis and image classification system that employs machine learning has been developed and applied to the PWQ/FEM flow to enhance the Process Stability Diagnosis (PSD) solution, which can now handle a significant volume of wafer images while realizing a 4X reduction in time to results. Moving the task of image analysis and classification to the computer has the added benefit of avoiding person-to-person inconsistencies in classification. The data flow consists of an automated machine learning-enabled process window analysis system that relies on CDSEM images taken on a FEM or Focus/Exposure Matrix wafer. We report results based on CDSEM images containing both contact hole and line features. The system enables full-wafer SEM image auto-classification and process window characterization.

引用

页数：8

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