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Breathing pattern has been shown to be different in chronic obstructive pulmonary disease (COPD) patients compared to healthy controls during rest and walking. In this study we evaluated respiratory parameters and the breathing variability of COPD patients as a function of their severity. Thoracic bioimpedance was acquired on 66 COPD patients during the performance of the six-minute walk test (6MWT), as well as 5 minutes before and after the test while the patients were seated, i.e. resting and recovery phases. The patients were classified by their level of airflow limitation into moderate and severe groups. We characterized the breathing patterns by evaluating common respiratory parameters using only wearable bioimpedance. Specifically, we computed the median and the coefficient of variation of the parameters during the three phases of the protocol, and evaluated the statistical differences between the two COPD severity groups. We observed significant differences between the COPD severity groups only during the sitting phases, whereas the behavior during the 6MWT was similar. Particularly, we observed an inverse relationship between breathing pattern variability and COPD severity, which may indicate that the most severely diseased patients had a more restricted breathing compared to the moderate patients. This work was supported in part by the Universities and Research Secretariat from the Ministry of Business and Knowledge/Generalitat de Catalunya under the Grant GRC 2017 SGR 01770, in part by the Agencia Estatal de Investigacion from the Spanish Ministry of Science ánd Innovation and the European Regional Development Fund, under the Grants RTI2018 098472-B-I00 and PID2021-126455OB-I00, and in part by the CERCA Programme/Generalitat de Catalunya.

Background and Objective: Chronic obstructive pulmonary disease (COPD) requires a multifactorial assess-ment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical perfor-mance measurements. Methods: Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These pa-rameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients' status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT out-comes. Results: Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong ( R = 0.84, MAPE = 8.10% for HRmax) and moderate ( R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classifica-tion of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions: We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personal-ized care. (c) 2022 The Authors. Published by Elsevier B.V. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie [grant number 846636]. This work was also supported in part by the Universities and Research Secretariat from the Generalitat de Catalunya [grant numbers GRC 2017 SGR 01770, FI-DGR], in part by the Agencia Estatal de Investigación from the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund [grant number RTI2018 098472-B-I00], and in part by the CERCA Programme/Generalitat de Catalunya.

EUV pellicles have been enabled to provide customers with defect protection for EUV reticles. However, due to the absorption that is much higher than for DUV pellicles, using the pellicle has certain disadvantages. Most significant is the reduction in throughput caused by the absorption of EUV photons in the pellicle. This leads to a customer decision to use a pellicle and accept the reduced throughput, or to not use a pellicle and have additional inspection steps to check the cleanliness of the EUV reticle. These tradeoffs vary by customer and use case. This study addresses the balance of factors for using or not using a pellicle through a cost comparison.

With the introduction of the NXE:3400B scanner, ASML has brought EUV to High-Volume Manufacturing (HVM). In this context, ASML is pursuing a dual-path approach towards zero reticle defectivity: EUV-compatible pellicle or zero particles towards reticle by advanced particle contamination control. This paper will focus on the latter approach of advanced particle contamination control and will show that we are able to reduce particle contamination towards reticle to a level that is compatible with HVM requirements for sub-10nm node lithography.