Adaptive on-line estimation and control of overlay tool bias

Modern lithographic manufacturing processes rely on various types of exposure tools, used in a mix-and-match fashion. The motivation to use older tools alongside state-of-the-art tools is lower cost and one of the tradeoffs is a degradation in overlay performance. While average prices of semiconductor products continue to fall, the cost of manufacturing equipment rises with every product generation. Lithography processing, including the cost of ownership for tools, accounts for roughly 30% of the wafer processing costs, thus the importance of mix-and-match strategies. Exponentially Weighted Moving Average (EWMA) run-by-run controllers are widely used in the semiconductor manufacturing industry. This type of controller has been implemented successfully in volume manufacturing, improving C-pk values dramatically in processes like photolithography and chemical mechanical planarization. This simple, but powerful control scheme is well suited for adding corrections to compensate for Overlay Tool Bias (OTB). We have developed an adaptive estimation technique to compensate for overlay variability due to differences in the processing tools. The OTB can be dynamically calculated for each tool, based on the most recent measurements available, and used to correct the control variables. One approach to tracking the effect of different tools is adaptive modeling and control. The basic premise of an adaptive system is to change or adapt the controller as the operating conditions of the system change. Using closed-loop data, the adaptive control algorithm estimates the controller parameters using a recursive estimation technique. Once an updated model of the system is available, model-based control becomes feasible. In the simplest scenario, the control law can be reformulated to include the current state of the tool (or its estimate) to compensate dynamically for OTB. We have performed simulation studies to predict the impact of deploying this strategy in production. The results for high running parts show rework reductions of about 10%, while low running parts improve by over 50%.