| Summary: | 碩士 === 國立成功大學 === 工程科學系專班 === 95 === In recent years, how to reduce the costs and enhance the qualified yield has become the direction of improvement for the semiconductor industry. The factors related to cost saving include the reduction of the wired width, increase of wafer size, enhancement of the qualified yield and the promotion of the overall efficiency of the equipment. Among those contributions, 12% -14% results from the reduction of wired width, only 2% results from the size of the wafer and only 1 % goes to the enhancement of the qualified yield. The rest of the contributions to cost reduction are attributed to the enhancement of the equipment efficiency. In order to improve above key indicators, the manufacturers always invest massive resources after installing their production equipment to increase the techniques of equipment operation, enhance the reliability and elasticity of the production line, and obtain the maximum qualified yield so as to reduce the costs. To reduce the rate of wafer scrap and the rate of non-qualified yield, the recipe on the production line is adjusted to make sure the quality of the products not to be impacted by the process drift of the equipment and reduce the consumption of the monitor wafers so as to result in the overall efficiency of the equipment. Over the past two decades, the technology and capacity of the semiconductor industry could be improved smoothly based on the forecast of the Mole Law. Thus while entering the ULSI era, the process of production is more hardly controlled due to the increase of wafer size, reduction of wired width and the complexity of technology day after day.
This study applies the Taguchi method with the data on the production line to obtain the optimal combination of the control factors as well as the effects of the control factors on the thickness of the film and the deviation of the process. The Taguchi method is adopted to establish the optimal linear regression model since there in no interaction among the experiment parameters. Furthermore, the two-stage Linear Regression model of EWMA controller is applied to control the thickness of the film in the diffusion process. In the first stage, the periodical maintenance Linear Regression model of EWMA controller is applied to make the process converge speedily. In the second stage, the stable Linear Regression model of EWMA controller is applied to stabilize the process so as to obtain the optimal method for accurately controlling the process.
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