Validity of the perturbation model for the propagation of MSF structures in 3D

Workflow for modeling of generalized mid-spatial frequency errors in optical systems

We propose a workflow for modeling generalized mid-spatial frequency (MSF) errors in optical imaging systems. This workflow enables the classification of MSF distributions, filtering of bandlimited signatures, propagation of MSF errors to the exit pupil, and performance predictions that differentiate performance impacts due to the MSF distributions. We demonstrate the workflow by modeling the performance impacts of MSF errors for both transmissive and reflective imaging systems with near-diffraction-limited performance.

Use of pupil-difference moments for predicting optical performance impacts of generalized mid-spatial frequency surface errors

In this work, we present a methodology for predicting the optical performance impacts of random and structured MSF surface errors using pupil-difference probability distribution (PDPD) moments. In addition, we show that, for random mid-spatial frequency (MSF) surface errors, performance estimates from the PDPD moments converge to performance estimates that assume random statistics. Finally, we apply these methods to several MSF surface errors with different distributions and compare estimated optical performance values to predictions based on earlier methods assuming random error distributions.

Pupil-difference moments for estimating relative modulation from general mid-spatial frequency surface errors

Standard surface specifications for mid-spatial frequency (MSF) errors do not capture complex surface topography and often lose critical information by making simplifying assumptions about surface distribution and statistics. As a result, it is challenging to link surface specifications with optical performance. In this work, we present use of the pupil-difference probability distribution (PDPD) moments to assess general MSF surface errors and show how the PDPD moments relate to the relative modulation.