کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5363908 | 1388308 | 2008 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Use of neural network method to characterize pressure controlled charge density of silicon nitride films deposited by PECVD
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
A prediction model of charge density of silicon nitride (SiN) films was constructed by using a generalized regression neural network (GRNN). The SiN film was deposited by a plasma enhanced chemical vapor deposition (PECVD) system and the deposition process was characterized by means of a statistical experiment. The prediction performance of GRNN was optimized by using a genetic algorithm (GA) and yielded an improved prediction of about 63% over statistical regression model. The optimized model was utilized to qualitatively investigate the effect of process parameters under various pressures. A refractive index model was effectively utilized to validate charge density variations. For the variations in process parameters, charge density was strongly dependent on [N-H]. Effects of NH3 or SiH4 flow rates were significant only under high collision rate. Effect of pressure-induced collision rate was noticeable only at higher NH3 flow rate or lower SiH4 flow rate.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Surface Science - Volume 254, Issue 15, 30 May 2008, Pages 4546-4551
Journal: Applied Surface Science - Volume 254, Issue 15, 30 May 2008, Pages 4546-4551
نویسندگان
Byungwhan Kim, Su Yeon Kim,