Background: In extreme ultraviolet lithography, maximum printable feature density is limited by stochastic defectivity. One of the methods to reduce it is the optimization of the aerial image via source mask optimization. To guide this optimization, we need to know which aerial image metric predicts defectivity. Feature variability is linked to defectivity.

Aim: Find which aerial image metric best predicts variability and defectivity.

Approach: We construct seven pupils that vary aerial image metrics [normalized image log slope (NILS), depth-of-focus (DoF), maximum intensity] in a controlled way. We measure variability and defectivity after development and after two different etch processes and correlate them to aerial image metrics.

Results: Stochastic critical dimension uniformity (CDU) is best predicted by NILS. Systematic CDU is determined by mask roughness through spatial frequency, with low frequencies differentiating outer and inner sigma pupils. Defectivity is best predicted by maximum intensity for missing holes and minimum intensity for merging holes. NILS and maximum intensity are strongly correlated. DoF has minimal impact.

Conclusions: Optimization toward maximum intensity reduces defectivity. Outer sigma pupils reduce variability. Increasing numerical aperture in next-generation EUV lithography is expected to reduce missing hole defects.