Reducing the noise below the shot-noise limit in sensing devices is one of the key promises of quantum technologies. Here, we study quantum plasmonic sensing based on an attenuated total reflection configuration with single photons as input. Our sensor is the Kretschmann configuration with a gold film, and a blood protein in an aqueous solution with different concentrations serves as an analyte. The estimation of the refractive index is performed using heralded single photons. We also determine the estimation error from a statistical analysis over a number of repetitions of identical and independent experiments. We show that the errors of our plasmonic sensor with single photons are below the shot-noise limit even in the presence of various experimental imperfections. Our results demonstrate a practical application of quantum plasmonic sensing is possible given certain improvements are made to the setup investigated, and pave the way for a future generation of quantum plasmonic applications based on similar techniques.
Process window qualification using focus-exposure wafers is an essential step in lithography and a key use case for CD-SEM metrology. An automated analysis using the correlation between CD and focus/dose is easily possible but rarely done due to missing safety checks. Pattern fidelity that is analyzed by eye and problematic focus/dose conditions that may cause pattern degradation are excluded by hand. Specifically, when EUV lithography is utilized for exposing the most critical layers, roughness estimation becomes much more important, as it will restrict the process window further. We develop and describe unbiased and stable roughness estimates for contact hole patterns and integrate them into the process window analysis pipeline and inline monitoring routine. The analysis goes beyond simple roughness values and can detect a variety of possible CD-SEM measurement problems and shape deviations as well. Furthermore, we introduce a novel image-based machine-learning approach to detect outliers and quantify defective or abnormal patterns. Notably, the underlying model does not require knowledge of the types of CD features or design information for which outliers should be detected. We demonstrate that the approach can reliably detect local defects and a variety of other pattern anomalies. Using the generated visualizations, images with anomalous features can be flagged automatically and the locations of the defects or deviations are pinpointed. The approach yields not only the final missing piece in automated process window qualification, but also new opportunities to monitor pattern fidelity in lithographical semi-conductor processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.