Subtle Anomaly Detection of Microscopic Probes using Deep learning based Image Completion

Autor:	Ira Leventhal, Deepika Neethirajan, Yiorgos Makris, Keith Schaub, Constantinos Xanthopoulos, Ikeda Kosuke
Rok vydání:	2019
Předmět:	Training set Multiple Models Manufacturing process business.industry Computer science Deep learning Process (computing) Anomaly detection Pattern recognition Artificial intelligence business Image (mathematics) Task (project management)
Zdroj:	ITC
Popis:	Automated defect inspection in manufacturing of microscopic probes is an important task and often requires machine learning driven solutions. A supervised only approach can be challenging, because production manufacturing process typically have few defects, thus large amounts of labeled training data are generally not available. In this work, we instead employed multiple models in a multi-step process to achieve the end goal of identifying defect and non-defect probe tips.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d184994a2fdecc10d2af54e1e00c154b https://doi.org/10.1109/itc44170.2019.9000126 Zobrazit plný text záznamu