Our solution is based on a machine learning (ML) model to predict blade wear out. It consists of two parts: a “data producer” part and a “data consumer” part.
Data producing is done to create trusted data sets (data products) which can then be consumed (queried, analyzed, modeled) on an analytics workspace by e.g. equipment engineers.
The first part contains the equipment interfaces, the data pipeline and the data products. We used EMR Serverless for its on-demand scaling, Glue, Python and Spark for integration, pre-processing and transformation of data, and DynamoDB and S3 buckets for storage.
The second part contains the analytics workspace with Sagemaker Studio to run the Machine Learning models. We used Athena Query service to analyze data directly in the S3 storage solution. PowerBI and Quicksight are used to visualize trends and predictions which in the end lead to triggers for engineers to replace a blade or adjust machine configurations.
The ML model is fed with equipment data such as dicing metrics, machine configurations, and product ID’s. The longer the model runs and the more data it can analyze, the better the quality of predictions becomes.
After a few months, the defect ratio from blade wear out was already cut in half. In the end, the defect ratio reached 0.9%: a 78% improvement. The specific 300mm wafer contains 300-400 dies, which are valued at 30 - 50 euro per die. Our solution thus saves over 400 euro per wafer on average. Even without considering the 5+ months of wafer production time that is no longer wasted, the ROI is already very interesting.