Use of neural network method to characterize pressure controlled charge density of silicon nitride films deposited by PECVD

✍ Scribed by Byungwhan Kim; Su Yeon Kim

• Book ID: 104002288
• Publisher: Elsevier Science
• Year: 2008
• Tongue: English
• Weight: 795 KB
• Volume: 254
• Category: Article
• ISSN: 0169-4332
• DOI: 10.1016/j.apsusc.2008.01.047

✦ Synopsis

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 NH 3 or SiH 4 flow rates were significant only under high collision rate. Effect of pressure-induced collision rate was noticeable only at higher NH 3 flow rate or lower SiH 4 flow rate.