WO3, SiO2, and TiO2 films with hydrophilic property are deposited by rf-magnetron sputtering. Their wettability is strongly depends on the presence or absence of the oxygen plasma etching on the glass substrates. The TiO2 film of 50 nm-thick on the plasma etched glass shows a water contact angle (WCA) below 5o which means a super-hydrophilic surface. However, WCA values are gradually degraded when the films are exposed under atmosphere, especially WO3. In order to improve hydrophilic property, the degraded films can be again recovered by UV illumination for 10 sec using UV-light and the TiO2 film shows a super-hydrophilic surface about 3o.

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.