Advanced CMP Process Control by Using Machine Learning Image Analysis 2021

被引：1

作者：

Hsu, Min-Hsuan ^{[1
]}

Lin, Chih-Chen ^{[1
]}

Yu, Hsiang-Meng ^{[1
]}

Chen, Kuang-Wei ^{[1
]}

Luoh, Tuung ^{[1
]}

Yang, Ling-Wuu ^{[1
]}

Yang, Ta-Hone ^{[1
]}

Chen, Kuang-Chao ^{[1
]}

机构：

[1] Macronix Int Co Ltd, Technol Dev Ctr, Hsinchu, Taiwan

来源：

IITC2021: 2021 IEEE INTERNATIONAL INTERCONNECT TECHNOLOGY CONFERENCE (IITC) | 2021年

关键词：

Machine Learning; Chemical-mechanical polishing; Closed Loop Control; Image Analysis; Grayscale; FILM THICKNESS;

D O I：

10.1109/IITC51362.2021.9537421

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Chemical-mechanical polishing closed loop control optimized process with machine learning assisted wafer image analysis algorithm implemented on the inter layer dielectric of 3D NAND ON stacking with poly-silicon stop layer is studied. The grayscale wafer image can be responded for film residue, stop layer damage, wafer edge damage, and thickness variation. Polishing five zones control model is trainned with wafer grayscale value by Python NN model with two hidden layers. The best condition of closed loop feedback control is deduced by machine learning assisted wafer image analysis algorithm.

引用

页数：4

共 14 条

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