Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data †

In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly divers...

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Detalles Bibliográficos
Autores principales: Chen, Chieh-Yu, Chang, Shi-Chung, Liao, Da-Yin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582566/
https://www.ncbi.nlm.nih.gov/pubmed/33023191
http://dx.doi.org/10.3390/s20195650

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582566/
https://www.ncbi.nlm.nih.gov/pubmed/33023191
http://dx.doi.org/10.3390/s20195650

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