Modeling the properties of PECVD silicon dioxide films using optimized back-propagation neural networks

Silicon dioxide films deposited by plasma-enhanced chemical vapor deposition (PECVD) are useful as interlayer dielectrics for metal-insulator structures such as MOS integrated circuits and multichip modules. The PECVD of SiO₂ in a SiH₄/N₂O gas mixture yields films with excellent physical properties. However, due to the complex nature of particle dynamics within the plasma, it is difficult to determine the exact nature of the relationship between film properties and controllable deposition conditions. Other modeling techniques, such as first principles or statistical response surface methods, are limited in either efficiency or accuracy. In this study, PECVD modeling using neural networks has been introduced. The deposition of SiO₂ was characterized via a 2^5-1 fractional factorial experiment, and data from this experiment was used to train feed-forward neural networks using the error back-propagation algorithm. The optimal neural network structure and learning parameters were determined by means of a second fractional factorial experiment. The optimized networks minimized both learning and prediction error. From these neural process models, the effect of deposition conditions on film properties has been studied, and sensitivity analysis has been performed to determine the impact of individual parameter fluctuations. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system. The models obtained will ultimately be used for several other manufacturing applications, including recipe synthesis and process control