The need for the accurate endpoint detection (EPD) technique in plasma etching process is increasing in the semiconductor industry for yield improvement and higher device performance. To monitor and analyze plasma process, the one of the most popular sensor is optical emission spectroscopy (OES). The OES can collect optical data emitted from plasma in the processing chamber and these data can be used in diverse methods for monitoring and analyzing plasma status. In this paper, a fuzzy inference system is applied for the endpoint detection using OES data. The fuzzy inference system is an interpretation method of uncertain data set. The normal etching stage and over-etching stage is classified by the fuzzy inference system using OES data. The proposed method successfully detected endpoint of silicon oxide film etching. The performance comparison between fuzzy inference system with linear regression method and with polynomial regression method is also investigated.
The endpoint detection (EPD) is the most important technique in plasma etching process. In plasma etching process, the Optical Emission Spectroscopy (OES) is usually used to analyze plasma reaction. And Plasma Impedance Monitoring (PIM) system is used to measure the voltage, current, power, and load impedance of the supplied RF power during plasma process. In this paper, a new decision making algorithm is proposed to improve the performance of EPD in SiOx single layer plasma etching. To enhance the accuracy of the endpoint detection, both OES data and PIM data are utilized and a newly proposed decision making algorithm is applied. The proposed method successfully detected endpoint of silicon oxide plasma etching.
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