Comparative Study on Experimental Design Methods for Optimization of Film Deposition Conditions in Semiconductor Manufacturing

As semiconductor design rules progress, the required level of reliability for semiconductor manufacturing equipment is increasing, and it is necessary to find the optimal process conditions that meets the film properties required by customers. The wafers are processed at high temperature close to 800°C in the deposition process equipment. The experimental design that takes into account interactions between two factors and quadratic term effects is required to find the optimal deposition process conditions, because thermal effects cause these effects. However, since the conventional one factor design experiment cannot reflect these effects on the model. In addition, since the batch deposition process takes nearly 8 hours, it is extremely costly to comprehensively experiment on many factors. In this study, we adopted D-optimal design and definitive screening design as methods of design of experiment for the process conditions optimization of deposition process. Since conducting additional experiments on actual equipment is costly, we built boosting neural network models as virtual semiconductor manufacturing equipment models by using the data from the past experiments. We derived 10 film properties using the Gaussian process regression (GPR) models built by one factor design, D-optimal design and definitive screening design. Then, we compared the differences between the film properties derived by each model and those required by customers. As a result, D-optimal design got the smallest error toward the film properties required by customers, and it was revealed that D-optimal design was suitable for deriving optimal manufacturing process conditions for semiconductor deposition process equipment.