Insight Extraction for Semiconductor Manufacturing Processes

S. Pampuri, G. A. Susto, J. Wan, A. Johnston, P. O'Hara, Seán McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationAutomation Science and Engineering (CASE), 2014 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages786-791
DOIs
Publication statusPublished - Aug 2014
Event2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014) - Taipei, Taiwan, Province of China
Duration: 18 Aug 201422 Aug 2014

Conference

Conference2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)
CountryTaiwan, Province of China
CityTaipei
Period18/08/201422/08/2014

Fingerprint Dive into the research topics of 'Insight Extraction for Semiconductor Manufacturing Processes'. Together they form a unique fingerprint.

  • Cite this

    Pampuri, S., Susto, G. A., Wan, J., Johnston, A., O'Hara, P., & McLoone, S. (2014). Insight Extraction for Semiconductor Manufacturing Processes. In Automation Science and Engineering (CASE), 2014 IEEE International Conference on (pp. 786-791). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CoASE.2014.6899415