NIST · ИТ · 708ee94236a768eb7599
Applications of machine learning at the limits of form-dependent scattering for defect metrology
Undetected patterning defects on semiconductor wafers can have severe consequences, both financially and technologically. Industry is challenged to find reliable and easy-to-implement methods for defect detection. In this paper we present robust machine
NIST · ИТОфициальный источник · 2019-03-26
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"title_en": "Applications of machine learning at the limits of form-dependent scattering for defect metrology",
"designation": "",
"series": "",
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"date": "2019-03-26",
"date_original": "March 26, 2019",
"url": "https://www.nist.gov/publications/applications-machine-learning-limits-form-dependent-scattering-defect-metrology",
"source": "NIST Publications",
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"authors_en": "Mark Alexander Henn , Hui Zhou , Richard M. Silver , Bryan M. Barnes",
"abstract_excerpt_en": "Undetected patterning defects on semiconductor wafers can have severe consequences, both financially and technologically. Industry is challenged to find reliable and easy-to-implement methods for defect detection. In this paper we present robust machine",
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{
"title_en": "Applications of machine learning at the limits of form-dependent scattering for defect metrology",
"designation": "",
"series": "",
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"date": "2019-03-26",
"date_original": "March 26, 2019",
"url": "https://www.nist.gov/publications/applications-machine-learning-limits-form-dependent-scattering-defect-metrology",
"source": "NIST Publications",
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"authors_en": "Mark Alexander Henn , Hui Zhou , Richard M. Silver , Bryan M. Barnes",
"abstract_excerpt_en": "Undetected patterning defects on semiconductor wafers can have severe consequences, both financially and technologically. Industry is challenged to find reliable and easy-to-implement methods for defect detection. In this paper we present robust machine"
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