• 한국전기전자재료학회논문지
• /
• 제22권9호
• /
• pp.741-746
• /
• 2009

Compare to conventional Indium Tin Oxide (ITO) film deposition methods, cesium assisted sputtering method has been shown superior electrical, mechanical, and optical film properties. However, it is not easy to use cesium assisted sputtering method since ITO film properties are very sensitive to Cesium assisted equipment condition but their mechanism is not yet clearly defined physically or mathematically. Therefore, to optimize deposited ITO film characteristics, development of accurate and reliable process model is essential. For this, in this work, we developed ITO film deposition process model using neural networks and design of experiment (DOE). Developed model prediction results are compared with conventional statistical regression model and developed neural process model has been shown superior prediction results on modeling of ITO film thickness, sheet resistance, and transmittance characteristics.

• 한국표면공학회지
• /
• 제40권4호
• /
• pp.165-169
• /
• 2007

Compared to conventional Indium Tin Oxide (ITO) film deposition methods, cesium (Cs) assisted sputtering offers higher film characteristics in terms of electrical, mechanical and optical properties. However, it showed highly non-linear characteristics between process input factors and equipment responses. Therefore, to maximize film quality, optimization of manufacturing process is essential and process characterization is the first step for process optimization. For this, we designed 2 level design of experiment (DOE) to analyze ITO film characteristics including film thickness, resistivity and transmittance. DC power, pressure, carrier flow, Cs temperature and substrate temperature were selected for process input variables. Through statistical effect analysis methods, relation between three types of ITO film characteristics and five kinds of process inputs are successfully characterized and eventually, it can be used to optimize Cs assisted sputtering processes for various types of film deposition.