Extreme Ultraviolet Lithography (EUVL) Quiz

The Development and Impact of Extreme Ultraviolet Lithography (EUVL)

• UV light with varying wavelengths, including 365nm, 248nm, 193nm, and eventually 13.8nm, was used in lithography technology development.
• Initial skepticism surrounded the feasibility of Extreme Ultraviolet (EUV) lithography due to the technology's absorption by glass and air, necessitating the use of mirrors in a vacuum.
• US Department of Energy-funded research at Livermore, Berkeley, and Sandia National Laboratories addressed technical obstacles and led to the invention of EUV technology.
• The Cooperative R&D Agreement (CRADA) between private companies and the Labs resulted in the formation of EUV-LLC, which held the rights to the technology.
• Licensing of the EUV technology was denied to Japanese companies, leading to ASML becoming the sole benefactor of the critical technology.
• ASML successfully deployed EUV-LLC's intellectual property, leading to the production of lithography machines capable of producing up to 200 wafers per hour by 2022.
• ASML's use of Zeiss optics, known for their precision, solidified the company's position as the world leader in lithography technology.
• ASML's record turnover of 18.6 billion € in 2021 surpassed competitors Canon and Nikon, who were denied access to the EUV technology.
• The US pressured Dutch authorities not to sell the EUV machines to China, and ASML is currently prohibited from shipping the machines to China under Dutch export controls.
• EUV photomasks work by reflecting light through alternating layers of molybdenum and silicon, and are mainly produced by AGC Inc. and Hoya Corporation.
• EUV lithography requires a vacuum due to the absorption of EUV radiation by all matter, and all optical elements must use defect-free molybdenum/silicon multilayers.
• An EUV tool comprises a laser-driven tin plasma light source, reflective optics with multilayer mirrors, and a hydrogen gas ambient to keep the EUV collector mirror free of tin deposition.

Dose-Response Relationship in Toxicology

• Dose-response evaluation involves plotting the percent of individuals responding as a function of dose for toxicological signs or symptoms and target organs.
• Dose-response curves are used in modeling studies to estimate probabilities of intended and unintended effects for a risk assessment scenario.
• The dose-response relationship is central in toxicology, guiding hazard assessment testing, modeling, and environmental regulations.
• Toxicological mechanism research aims to clarify the underlying basis for dose-dependent transitions.
• The dose-response relationship is fundamental to toxicological research and applications.
• Dose-response modeling (DRM) involves six basic steps, including data selection, model selection, statistical distribution assumption, and parameter determination.
• Empirical and biologically based models are two categories of dose-response models, with most focusing on empirical models.
• Statistical distribution is assumed for the response and used to derive a mathematical function describing the fit of the model to the data.
• The implementation of dose-response analysis involves extrapolating results to other exposure scenarios and doses, including from animals to humans.
• Dose-response models can be simple (e.g., linear) or complicated (e.g., biologically based), often needing different models to describe different parts of the relationship.
• Different types of data (quantal response, count data, continuous response, categorical measure) require different methods for modeling changes in response beyond the control response.
• Dose-response models are used by public health agencies for risk assessment and decisions on banning or limiting exposure to hazardous agents, with a major impact in limiting exposure.

Dose–Response Modeling and Analysis in Toxicology

• Dose–Response Modeling (DRM) can be used to identify a low-risk level of exposure by extrapolating known responses.
• DRM involves five basic steps that can be repeated with different parameterization to understand the impact on predictions.
• The final step in DRM (step 6) is aimed at understanding sensitivity and judging the overall quality of predictions.
• Sensitivity analysis can focus on assumptions used in the DRA or implementation and is performed either before or after step 5.
• DRM is regarded as an adequate approach for analyzing dose–response data in animal and human studies.
• DRM is used for calculating a benchmark dose (BMD) based on the entire dose–response curve, offering advantages over NOAELs and LOAELs.
• The BMD derived from DRM allows for quantifying uncertainties and may be used for exposures exceeding guidance and guideline values.
• Dose response and dose effect are fundamental descriptors of events following exposure in toxicology.
• Newer areas of study, such as genomics, proteomics, and metabolomics, aim to apply technologies to evaluate or monitor patient health based on dose-response behavior.
• Dose–response models describe a cause–effect relationship and can range from simple to complex multicompartment toxicokinetic/toxicodynamic models.
• Model uncertainties in dose–response analysis may be represented by probability trees and rely on expert opinion or algorithms for evaluation.
• Dose-response analysis should focus on the critical effect in the most sensitive subsection of the population.