In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.
|Title of host publication
|Automation Science and Engineering (CASE), 2014 IEEE International Conference on
|Institute of Electrical and Electronics Engineers Inc.
|Published - Aug 2014
|2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014) - Taipei, Taiwan, Province of China
Duration: 18 Aug 2014 → 22 Aug 2014
|2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)
|Taiwan, Province of China
|18/08/2014 → 22/08/2014