Industry 4.0 technologies are being deployed to computerize and digitize processes across many industries. This trend has not been lost in semiconductor manufacturing, where customers are asking for Smart Manufacturing solutions that connect fabs, equipment and materials across the supply chain. The goal of this effort has been to assess performance on every wafer, improve productivity of equipment, and achieve faster time to yield. To achieve this vision, equipment vendors will need to invest in sensors, virtual processing, big data architectures, software and controls, robotics, and advanced services. These investments will make their equipment more productive and allow manufacturers to integrate this equipment more productively into the larger semiconductor manufacturing environment.
In this talk, we will discuss the role of high-speed data platforms, robotics, equipment automation, integrated sensors and metrology, process modeling and advanced services in the future of Smart Manufacturing. We will also review how data mining and machine learning are being applied to analyze complex data, predict wafer and equipment performance, and achieve faster time to yield. Specific examples of machine learning in semiconductor manufacturing, such as diagnostic-to-chamber matching and virtual metrology data generation, will be highlighted.