Abstract
Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.
Original language | English |
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Title of host publication | 26th Irish Signals and Systems Conference (ISSC), 2015 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9781467369749 |
DOIs | |
Publication status | Published - 25 Jun 2015 |
Event | 26th Irish Signals and Systems Conference - Carlow, Ireland Duration: 24 Jun 2015 → 25 Jun 2015 |
Conference
Conference | 26th Irish Signals and Systems Conference |
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Country | Ireland |
City | Carlow |
Period | 24/06/2015 → 25/06/2015 |