Understanding the Significance of Local Variability in Defect-Aware Process Windows

Abstract

Process windows based on Mean CD (“Critical Dimension”) have been an analysis workhorse for estimating and comparing the robustness of semiconductor microlithography processes for more than 30 years. While tolerances for variation of CD are decreasing in step with the target CD, the acceptable number of printed defects has remained flat (Hint: Zero) even as the number of features increases quadratically. This disconnect between two key process estimators, CD variability and defect rate, must be addressed. At nodes that require EUV lithography, estimating the printed defects based solely on a Mean CD process window is no longer predictive. The variability / distribution of the printed CDs must be engineered so that there are no failures amongst the billions of instances, rendering the Mean CD, often measured on just hundreds or thousands of instances, a poor predictor for outliers. A “defect-aware” process window, where the count of printed defects is considered in combination with more advanced statistical analysis of measured CD distributions can provide the needed predictability to determine whether a process is capable of sufficient robustness. Determining process robustness where stochastics and defects are taken into account can be simplified by determining the CD process margin. In this work we study dense via/contact hole arrays exposed with 0.33NA single exposure EUV lithography after both the lithography and etch steps. We describe a methodology for expanding the analysis of process windows to include more than the mean and $3{{\sigma }}$ of the data. We consider the skew and kurtosis of the distribution of measured CD results per focus-exposure condition and compare/correlate the measured CD process window results to the CD process margin.