Buch, Englisch, 100 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 197 g
Big Data Analytics and Industry 4.0 Applications
Buch, Englisch, 100 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 197 g
Reihe: SpringerBriefs in Applied Sciences and Technology
ISBN: 978-3-031-14064-8
Verlag: Springer International Publishing
This book systematically analyzes the applicability of big data analytics and Industry 4.0 from the perspective of semiconductor manufacturing management. It reports in real examples and presents case studies as supporting evidence. In recent years, technologies of big data analytics and Industry 4.0 have been frequently applied to the management of semiconductor manufacturing. However, related research results are mostly scattered in various journal issues or conference proceedings, and there is an urgent need for a systematic integration of these results. In addition, many related discussions have placed too much emphasis on the theoretical framework of information systems rather than on the needs of semiconductor manufacturing management. This book addresses these issues.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
- Naturwissenschaften Physik Elektromagnetismus Halbleiter- und Supraleiterphysik
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
Weitere Infos & Material
Chapter 1. Big Data Analytics for Semiconductor Manufacturing.- Chapter 2. Industry 4.0 for Semiconductor Manufacturing.- Chapter 3. Cycle Time Prediction and Output Projection.- Chapter 4. Defect Pattern Analysis, Yield Learning Modeling and Yield Prediction.- Chapter 5. Job Sequencing and Scheduling.