Automated Surface Classification of SRF Cavities for the Investigation of the Influence of Surface Properties onto the Operational Performance

Abstract

Superconducting niobium radio-frequency cavities are fundamental for the European XFEL and the International Linear Collider. To use the operational advantages of superconducting cavities, the inner surface has to fulfill quite demanding requirements. The surface roughness and cleanliness improved over the last decades and with them, the achieved maximal accelerating field. Still, limitations of the maximal achieved accelerating field are observed, which are not explained by localized geometrical defects or impurities. The scope of this thesis is a better understanding of these limitations in defect free cavities based on global, rather than local, surface properties.For this goal, more than 30 cavities underwent subsequent surface treatments, cold RF tests and optical inspections within the ILC-HiGrade research program and the XFEL cavity production. An algorithm was developed which allows an automated surface characterization based on an optical inspection robot. This algorithm delivers a set of optical surface properties, which describes the inner cavity surface. These optical surface properties deliver a framework for a quality assurance of the fabrication procedures. Furthermore, they shows promising results for a better understanding of the observed limitations in defect free cavities.