Application of pattern recognition in mix-and-match lithography

Mix-and-match lithography continues to gain acceptance as a valuable strategy for reducing capital costs and increasing throughput productivity in semiconductor manufacturing. The successful implementation of mix-and-match lithography requires consideration of the unique characteristics of all systems being matched. Among these issues are alignment target placement and alignment strategy. The alignment system for each stepper manufacturer uses specially designed targets for wafer alignment. However, the wafer area available for dedicated alignment targets is typically restricted to maximize the quantity of production die per wafer. One approach to remove the target area limitation is to implement an alignment system based on pattern recognition techniques. Using pattern recognition, the optical image of a specified field structure is digitized and interpreted as an alignment target. Since pattern recognition has the potential to learn and interpret various structures, it provides great flexibility for using alignment structures from other steppers or possibly device structures. This capability minimizes the wafer area required for alignment targets. Typically, there are two steps involved in wafer alignment in mix-and-match lithography. First is wafer global alignment which is necessary to remove coarse grid placement errors due to wafer loader matching. The second step is the fine or precision alignment of the stepper field prior to exposure. A pattern recognition system provides the capability for either or both of these alignment procedures. In this study, a pattern recognition system on a Ultratech 2244i stepper is used to demonstrate the elimination of global alignment targets for mix-and-match lithography. The characterization wafers are patterned with a 5x reduction stepper for the first level. Using the first level wafers from this stepper, a performance comparison is made to evaluate the flexibility of the pattern recognition system on a variety of pattern features and substrate films for global alignment. The pattern recognition repeatability and reliability for the case of global alignment is determined. In addition, the MVS capture time and the robustness of the MVS search routines are evaluated as a function of global grid errors.