Use of neural network method to characterize pressure controlled charge density of silicon nitride films deposited by PECVD |
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Authors: | Byungwhan Kim Su Yeon Kim |
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Affiliation: | Department of Electronic Engineering, Sejong University, 98, Goonja-Dong, Kwangjin-Gu, Seoul 143-747, Republic of Korea |
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Abstract: | 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. |
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Keywords: | Silicon nitride film Charge density Plasma enhanced chemical vapor deposition Neural network Model |
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