UC Berkeley

Modern applications of optics, especially those which require shorter wavelengths of light, place ever-increasing demands on the performance of optical tools and systems. Working with extreme ultraviolet, soft x-ray and hard x-ray light poses complex limitations and challenges to diagnosing and maintaining diffraction-limited performance by measuring and controlling optical aberrations. By utilizing computational methods such as optimization and machine learning, we show that some of these limitations can be circumvented without sacrificing accuracy or precision.

In this work, we discuss a method for aberration measurement that is based on an analysis of speckle images acquired in situ. By using a stationary random object, our method eliminates the need for precise manufacturing and alignment of a test target. Moreover, the method provides a full, dense characterization of the optical system under test using relatively few images. The method has been successfully applied to an EUV microscope system, and is shown to be accurate to within λ/180. We also discuss a method for aberration compensation via the characterization and control of an adaptive optical element for x-ray optical systems. Adaptive x-ray optics are a relatively new technology, and our work aims to enable their use within the specifications of synchrotron beamline systems. To this end, we demonstrate the ability to experimentally predict and control the behavior of the glancing-incidence deformable mirror surface to within 2 nm rms, allowing the application of sub-wavelength corrections to an incident wavefront.