Materials informatics for process and material co-optimization

Abstract

In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results within reasonable development duration and acceptable cost. Unfortunately, it is a very time-consuming procedure, because the number of possible combinations of process/material candidates is very large. Here, we develop a methodology for co-optimization of processes and their materials. We successfully constructed a prediction model for dry-etching of high-k materials (R2 = 0.65). Also, it was proven that considering both the materials and processes is needed for accurate prediction of etching rates. By trying only <0.00001% of all possible process/material candidates with this model and Bayesian optimization, we can find new combinations of gasses and their processes for more than 100 times higher etching rates than that with a traditional gas/process condition. Furthermore, we discussed that accurate prediction can be made by using a combination of the Bayesian optimization with LASSO and materials knowledge from related scientific papers. Future work will focus on validating the versatility of our methodology by applying it to other development items.