Tool Difference Transition Visualization for Semiconductor Manufacturing

Reducing tool differences is one of the important tasks to improve the yield of semiconductor device manufacturing. Various kinds of methods have been proposed to solve this problem. These methods enable us to find problematic tools from numbers of process tools. However, finding problematic tools is not always enough to work out the problem of tools. Visualization of the transition profile of tool differences provides us additional information to help finding the unknown control parameters. It helps us to find corresponding events such as maintenance, repair, and adjustment. We design a profiling algorithm based on the maximum likelihood estimation. This algorithm introduces a new constraint that “tools are stable at each process step for a short period”, and recursively uses a regression method by employing previous results of the estimation. Simulation results show that our algorithm extracts the transition profile of tool differences more precisely than existing regression methods.