Extreme learning machines for virtual metrology and etch rate prediction
| Autor: | Luca Puggini, Seán McLoone |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Engineering
Artificial neural network business.industry Semiconductor device fabrication Process (computing) Machine learning computer.software_genre Predictive maintenance Fault detection and isolation Reliability engineering Metrology Lasso (statistics) Virtual metrology Artificial intelligence business computer |
| Zdroj: | 2015 26th Irish Signals and Systems Conference (ISSC). |
| DOI: | 10.1109/issc.2015.7163771 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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