Endpoint Detection in Plasma Etching Using Principal Component Analysis and Support Vector Machine

Autor:	Seung-Soo Han, Sang-Jeen Hong, Yi-Seul Han, Young-Kook Park
Rok vydání:	2012
Předmět:	Support vector machine Plasma etching Computer science business.industry Principal component analysis Pattern recognition Artificial intelligence business
Zdroj:	ECS Transactions. 44:1075-1080
ISSN:	1938-6737 1938-5862
DOI:	10.1149/1.3694431
Popis:	Since semiconductor devices are extremely integrated, process control is much more difficult in semiconductor fabrication. The optical emission spectroscopy (OES) acquires unique wavelength intensity of particles in plasma chamber, and the endpoint can be decided by utilizing its intensity. However, the endpoint detection is difficult because not only of these tremendous amount of data, but also of extremely large amount of noise in OES data. To solve these problems, the OES data of by-products and the data of etchants are classified by SNR. And a combination of Principal Component Analysis (PCA) and Support Vector Machine (SVM) hybrid algorithm is applied to detect endpoint. The PCA was utilized to reduce dimension of the selected data, and SVM algorithm is applied to separates status between before endpoint and after endpoint. The SVM model using SNR and PCA showed excellent performance in real-time endpoint detection in plasma etching.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e509834e0408208b3a055711d6ccd286 https://doi.org/10.1149/1.3694431 Zobrazit plný text záznamu