Digital twins are virtual representations of physical systems. The current interest in them is fueled by the convergence of IoT, machine learning and big data technology. As process complexity increases, they are becoming key to efficient operations and high product yields.
There is now a demand for ‘wide-and-big data’ analytic solutions that detect associations between product quality metrics and thousands to millions of process variables. These cutting edge solutions can support root-cause and predictive analyses. Further, the results must be available close to "real-time" to enable useful process interventions — for example to identify subtle equipment changes, process shift or drift, or to predict and remedy substandard yield for a lot in the line.
This webinar focuses on the implementation of a semiconductor manufacturing digital twin for yield that detects associations between product quality metrics and up to millions of predictor process.
What you will see via demos and learn about:
- How hybrid big-data plus in-memory systems are being utilized to address the various new analytic and IT-architecture problems associated with this challenge
- How to combine large-scale distributed analytics capabilities with comprehensive server- and in-memory-based advanced analytics
- How to deliver actionable interactive results through intelligent visualizations.
Mike Alperin, Manufacturing Industry Consultant
Steven Hillion, Sr. Director Data Science