In the process of process characterization, defining the Design Space is a very important step. Monte Carlo simulation is a very good way to define the Design Space, but in this process, it is often necessary to modify the upper and lower limits of the process parameters many times and repeatedly, and manual modification often fails to produce reasonable results. So, is there a better way to automatically obtain the Design Space? This speech will demonstrate how to automatically obtain the Design Space through machine learning methods through cases , providing ideas and references for everyone.
