Privacy Preserving Amalgamated Machine Learning for Process Control

摘要

Further application of machine learning is important for the future development of semiconductor fabrication. Machine learning relies on access to large, detailed datasets. When different parts of the data are owned by different companies who do not wish to pool their data due to commercial sensitivity concerns, the benefits of machine learning can be limited resulting in reduced manufacturing performance. Imec has developed Privacy-preserving Amalgamated Machine Learning (PAML) to overcome this problem and achieve predictive performance close to models built on pooled data, without compromising sensitive raw data. In this paper we give a concrete example based on an in-house overlay metrology dataset where we apply a PAML enhanced version of a tree regression model, and quantify the performance benefit compared to separate models that don't have access to all of the data.