As today’s business environment becomes increasingly competitive and the volume, velocity, and complexity of data explode, especially in semiconductor industry, companies are facing challenges in adapting to a digital age when dealing with big data issues for rapid decision making and improved productivity. To address these challenges and prepare for the transformation, U.S. has been driving Cyber Physical Systems (CPS), Industrial Internet, and Advanced Manufacturing Partnership (AMP) Program to define and advance future manufacturing. Germany is leading a transformation towards the 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical Production System (CPPS). It is clear that as more predictive analytics software and embedded Internet of Things (IoT) solutions are integrated in industrial equipment and products, predictive technologies can further intertwine smart IoT to predict tool degradation and product performance autonomously and further optimize the smart service systems. However, many Smart Manufacturing systems today are not ready to manage industrial big data and deliver sustainable performance due to the lack of scalable and robust smart analytics. The development of smart systems also tends to be ad hoc and isolated rather than systematic and extensible.
This presentation aims at addressing the readiness and trends of predictive big data analytics in industrial context and cyber-physical modeling for future Smart Manufacturing. Advanced prognostic technologies to achieve machine health transparency, improve process quality, and increase factory productivity will be introduced with brief discussion on successful industrial applications. A case study in advanced process control leveraging big data and prediction will be presented as a real-world example in semiconductor manufacturing predictive maintenance.