Run-to-run overlay control of steppers in semiconductor manufacturing systems based on history data analysis and neural network modeling

This paper presents a new run-to-run control scheme to reduce overlay misalignment errors in steppers and demonstrates the feasibility of the scheme by real-time experimental tests. The overlay misalignment error mainly depends on two factors: one is the internal dynamics of photo processes and steppers and the other is the process history of lots, such as base equipment and reticles. Based on these observations, a new control scheme was designed to find the stepper inputs minimizing the misalignment errors based on history data analysis and neural network models. Moreover, we demonstrated that the proposed control scheme reduces the spec-out ratios as well as the number of engagements of the send-ahead wafer process, which thereby results in the increase of product yield in semiconductor manufacturing.