Advanced process control for Hyper-NA lithography based on CD-SEM measurement

With the recent introduction of immersion lithography, optical systems with numerical aperture (NA) reaching 1.0 or larger can be realized. Various Resolution Enhancement Techniques (RET) such as various phase shift mask approaches have been used to push even further the resolution limit by reducing k(1) scaling factor, including Double Patterning Technology. However, with the improved resolution by Hyper-NA and Low-k(1), lithographers face the problem of decreasing Depth of Focus and in turn reduced process latitude. Throughout the industry, Process Window has been widely used as an analytical tool to evaluate process latitude for a given design feature size; therefore, the ability to accurately and efficiently derive a Process Window within which a process can run on target and in control is fundamental to Low-k(1) lithography. Accuracy of Process Window derivation is based on the ability to accurately measure and model the physical dimension of the design feature and how it changes in response to changes in process parameters. In the case of lithography, the Process Window of a desired critical dimension target is bounded by changes in exposure energy and defocus. To be able to accurately measure the physical dimension of the design feature remains a big challenge for metrologists especially in the presence of other process noise. In this work, it is shown that the precision of PW measurement can be enhanced by using CD-ACD (Average CD) function to measure a FEM (Focus-Exposure matrix) wafer. ACD is a function, which simultaneously measures several points, thus providing higher precision measurement in comparison to the conventional single point measurement. As seen in this work, by using ACD measurements to derive the Process Window, there is a significantly improvement in the stability of the derived Process Window. Also reported is the MPPC (Multiple Parameters Profile Characterization) (*1)), a function which provides the ability to extract pattern shape information from a measured e-beam signal. This function together with the ACD function enables PW measurement with high precision, which also takes into account the actual pattern shape. PW derived from conventionally measured data was compared with PW derived from ACD and MPPC measurement and we were able to demonstrate an improvement of more than 30% in precision of PW determination.