Modeling the growth of PECVD silicon nitride films for solar cell applications using neural networks

Abstract

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflection coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks, PECVD nitride films are known to contain hydrogen, and defect passivation by hydrogenation enhances efficiency in polycrystalline silicon solar cells. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring Si/sub 3/N/sub 4/ film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, silicon nitride PECVD modeling using neural networks has been investigated. The deposition of Si/sub 3/N/sub 4/ was characterized via a central composite experimental design, and data from this experiment was used to train optimized feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. It was found that the process parameters critical to increasing hydrogenation and therefore enhancing carrier lifetime in polysilicon solar cells are temperature, silane, and ammonia flow rate. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system.