A systematic approach to secure data collection across an OEM's fleet of tools

This paper discusses the approaches that a team at PEER Group took to design and implement a secure data management system intended to collect data from tools installed at various fabs in different geographic locations and to move that data to a cloud-based storage system. The motivation for the work was to find the best way to match equipment performance across a global fleet of tools using modern analytics on the data to enable predictive decision-making. Gathering data and feeding it into remote analytics software to perform fleet-wide comparisons presents familiar obstacles related to IP protection, the management of big data, and implementation risk. The majority of the effort in creating such a data collection system did not lie in the collection or movement of the data, but rather in the systematic identification and assessment of objections to data sharing in a notoriously secretive industry. By explicitly addressing each of the concerns related to secure data sharing, we were able to create a system that allowed for limited collection of data in a means acceptable to all stakeholders.