• International Journal of Extreme Manufacturing
  • Vol. 3, Issue 3, 35104 (2021)
Yun Chen1、2、*, Yanhui Chen1, Junyu Long1, Dachuang Shi1, Xin Chen1, Maoxiang Hou1, Jian Gao1, Huilong Liu1, Yunbo He1、3, Bi Fan4, Ching-Ping Wong2、5, and Ni Zhao2
Author Affiliations
  • 1State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, School of Electromechnical Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
  • 2School of Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
  • 3Guangdong ADA Intelligent Equipment Ltd, Foshan 510006, People’s Republic of China
  • 4Institute of Business Analysis and Supply Chain Management, College of Management, Shenzhen University, Shenzhen, People’s Republic of China
  • 5School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
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    DOI: 10.1088/2631-7990/abff6a Cite this Article
    Yun Chen, Yanhui Chen, Junyu Long, Dachuang Shi, Xin Chen, Maoxiang Hou, Jian Gao, Huilong Liu, Yunbo He, Bi Fan, Ching-Ping Wong, Ni Zhao. Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning[J]. International Journal of Extreme Manufacturing, 2021, 3(3): 35104 Copy Citation Text show less
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    Yun Chen, Yanhui Chen, Junyu Long, Dachuang Shi, Xin Chen, Maoxiang Hou, Jian Gao, Huilong Liu, Yunbo He, Bi Fan, Ching-Ping Wong, Ni Zhao. Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning[J]. International Journal of Extreme Manufacturing, 2021, 3(3): 35104
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