NIST · ИТ · 3027f4fff980e24cf579
Contrasting Conventional and Machine Learning Approaches to Optical Critical Dimension Measurements
Accurate, optics-based measurement of feature sizes at deep sub-wavelength dimensions has been conventionally challenged by improved manufacturing, including smaller linewidths, denser layouts, and greater materials complexity at near-atomic scales
NIST · ИТОфициальный источник · 2020-03-24
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"title_en": "Contrasting Conventional and Machine Learning Approaches to Optical Critical Dimension Measurements",
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"series": "",
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"date": "2020-03-24",
"date_original": "March 24, 2020",
"url": "https://www.nist.gov/publications/contrasting-conventional-and-machine-learning-approaches-optical-critical-dimension",
"source": "NIST Publications",
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"authors_en": "Bryan M. Barnes , Mark Alexander Henn",
"abstract_excerpt_en": "Accurate, optics-based measurement of feature sizes at deep sub-wavelength dimensions has been conventionally challenged by improved manufacturing, including smaller linewidths, denser layouts, and greater materials complexity at near-atomic scales",
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"title_en": "Contrasting Conventional and Machine Learning Approaches to Optical Critical Dimension Measurements",
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"date": "2020-03-24",
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"authors_en": "Bryan M. Barnes , Mark Alexander Henn",
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