Materials Informatics for Process and Material Co-Optimization

In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results. 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). 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 15-20 times higher etching rates than that with a traditional gas/process condition.