Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms

Silicon oxide (SiO₂) films have extensive applications in integrated circuit fabrication technology, including passivation layers for integrated circuits, diffusion or photolithographic masks, and interlayer dielectrics for metal-insulator structures such as MOS transistors or multichip modules. The properties of SiO₂ films deposited by plasma enhanced chemical vapor deposition (PECVD) are determined by the nature and composition of the plasma, which is in turn controlled by the deposition variables involved in the PECVD process. The complex nature of particle dynamics within a plasma makes it very difficult to quantify the exact relationship between deposition conditions and critical output parameters reflecting film quality. In this study, the synthesis and optimization of process recipes using genetic algorithms is introduced. In order to characterize the PECVD of SiO₂ films deposited under varying conditions, a central composite designed experiment has been performed. Data from this experiment was then used to develop neural network based process models. A recipe synthesis procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities, including zero stress, 100% uniformity, low permittivity, and minimal impurity concentration. This synthesis procedure utilized genetic algorithms, Powell´s algorithm, the simplex method, and hybrid combinations thereof. Recipes predicted by these techniques were verified by experiment, and the performance of each synthesis method are compared. It was found that the genetic algorithm-based recipes generally produced films of superior quality. Deposition was carried out in a Plasma Therm 700 series PECVD system