Construction of a Prediction Model of Wafer Temperature and Film Thickness in a Multiwafer LPCVD Furnace

碩士 === 長庚大學 === 化工與材料工程學系 === 99 === The behavior and characteristics of the low pressure chemical vapor deposition (LPCVD) furnace used in semiconductor manufacturing industries are studied in this work. It is well known the wafer temperatures have significant effects on film thickness. As the wafe...

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Bibliographic Details
Main Authors: Young Soung Cheng, 莊詠淞
Other Authors: G. B. Wang
Format: Others
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/13517370404536089563
Description
Summary:碩士 === 長庚大學 === 化工與材料工程學系 === 99 === The behavior and characteristics of the low pressure chemical vapor deposition (LPCVD) furnace used in semiconductor manufacturing industries are studied in this work. It is well known the wafer temperatures have significant effects on film thickness. As the wafer temperatures can not be directly measured, the wafer temperatures are controlled by adjusting furnace temperatures. Therefore, the contents of this work are classified into two parts. The first part is about the wafer temperature distribution inside the furnace called thermal model. The second part is for the wafer deposition thickness distribution inside the furnace called deposition model. To get a precise wafer deposition model, we modify the PFR deposition model proposed by Chen(2005). First, by comparing no etching area condition studied by Huang(2010), one can realize how changes in etching area affect deposition rates. Second, to improve the prediction performance of the modified PFR deposition model, the optimal rate constant values are presented by effectively fitting the experimental data reported by Badgwell et al.(1992). In practice, in the manufacturing process of wafer, the LPCVD furnace belongs to a batch type process. Post-process wafer thickness measurements made after each run are used along with empirical process models and drift compensation and noise rejection techniques to manipulate new equipment recipes for the next run. Here, by combining the wafer thermal model and modified PFR deposition model mentioned above, a simple and effective prediction model is finally presented as a good reference for the engineers and operators.