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Arjen Boogaard was born on September 10th, 1971 in Koudekerke. He received the Ph.D. degree in electrical engineering from the University of Twente, Enschede, The Netherlands, in 2011. He joined Philips Research in 1996 where he co-developed a portable electrostatic air cleaner and studied the adhesion of polymers and phosphors on CRT screens. He then became involved in thin film technology. He contributed to the development of a transceiver IC for cellular telephones, which was successfully transferred to a production facility. He also worked on electron beam lithography and on ASML lithography systems. From 2004 to 2010 he was employed by the MESA+ Institute for Nanotechnology, chair of Semiconductor Components, University of Twente. His research there enabled the fabrication of high-quality silicon dioxide gate dielectrics using PECVD technology. He is currently employed at ASML, Veldhoven, The Netherlands, studying defectivity improvements of immersion lithography scanners, and the lifetime of optical elements in extreme ultraviolet lithography. He holds eight U.S. patents. Arjen Boogaard is a Senior Member of the IEEE.

Recent results

Semiconductor equipment suppliers are asked to build tools that can operate almost continuously. Once such a reliable tool is developed, defect data collection, understanding, and reduction become increasingly important since the tool has to manufacture reliable (layers of) semiconductor devices that scarcely fail. At this stage, semiconductor equipment suppliers could benefit from the concepts of chip yield modeling, because it directly relates their defect data to the yield of semiconductor devices.
We presented a study in which we estimated the yield impact of defects related to immersion photolithography scanners. Defects were separated into various classes, and the size distributions of those classes were measured. Given a circuit’s critical area, forecasting of the yield for any defect class became straightforward, and also yield predictions could be made for future technology nodes, resulting in the optimal choice of yield-enhancing strategies.