Integrated model-based run-to-run uniformity control for epitaxial silicon deposition.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Also available online at the MIT Theses Online homepage Includes bibliographical references (p. 241-247).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Semiconductor fabrication facilities require an increasingly expensive and integrated set of processes. The bounds on efficiency and repeatability for each process step continue to tighten under the pressure of economic forces and product performance requirements. This thesis addresses these issues and describes the concept of an "Equipment Cell," which integrates sensors and data processing software around an individual piece of semiconductor equipment. Distributed object technology based on open standards is specified and utilized for software modules that analyze and improve semiconductor equipment processing capabilities. A testbed system for integrated, model-based, run-to-run control of epitaxial silicon (epi) film deposition is developed, incorporating a cluster tool with a single-wafer epi deposition chamber, an in-line epi film thickness measurement tool, and off-line thickness and resistivity measurement systems. Automated single-input-single-output, run-to-run control of epi thickness is first demonstrated. An advanced, multi-objective controller is then developed (using distributed object technology) to provide simultaneous epi thickness control on a run-to-run basis using the in-line sensor, as well as combined thickness and resistivity uniformity control on a lot-to-lot basis using off-line thickness and resistivity sensors.(cont.) Control strategies are introduced for performing combined run-to-run and lot-to-lot control, based on the availability of measurements. Also discussed are issues involved with using multiple site measurements of multiple film characteristics, as well as the use of time-based inputs and rate-based models. Such techniques are widely applicable for many semiconductor processing steps.by Aaron Elwood Gower-Hall.Ph.D

Computer-Integrated Design and Manufacture of Integrated Circuits

Contains reports on three research projects.Defense Advanced Research Projects Agency DABT 63-95-C-0088Defense Advanced Research Projects Agency N00174-93-K-0035Stanford UniversityLeaders for Manufacturing Progra

Computer-Assisted Prototyping of Advanced Microsystems

Contains reports on five research projects.Defense Advanced Research Projects Agency Contract DABT 63-95-C-0088Stanford Universit

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Ecological Survey of Tauranga Harbour

This report summarises the results of biological and physical data collected from a broad scale intertidal survey of Tauranga Harbour conducted between December 2011 and February 2012. The survey was designed to understand more fully the role of various anthropogenic stressors on the ecology of the harbour. The research was conducted as part of the Manaaki Taha Moana (MTM) programme. The wider research project aims to restore and enhance coastal ecosystems and their services of importance to iwi/hapū, by working with iwi to improve knowledge of these ecosystems and the degradation processes that affect them. In this report we assess the health of macrofaunal benthic communities (bottom-dwelling animals) as well as trends in sediments, nutrients and contaminants. The results indicate that the sites identified as most impacted were generally located in the upper reaches of estuaries in some of the locations least exposed to wind, waves and currents. In addition, the biological community composition characterising sites with different sediment textures, nutrient and contaminant loadings were found to vary. Sediments within Tauranga Harbour were predominantly sandy with the percentage of mud within a similar range as measured for other New Zealand estuaries. The exceptions included Te Puna Estuary and Apata Estuary, which experience higher rates of sedimentation. Heavy metal contamination in sediments is often highly correlated with the percentage of mud content due to the adherence of chemicals to fine sediments and/or organic content. It is, therefore, not surprising that heavy metal concentrations were also highest in the depositional inner areas of the harbour, such as Te Puna Estuary. The heavy metal contaminant levels within Tauranga were well below relevant guideline thresholds and lower than concentrations measured in many other estuaries in New Zealand and overseas. Although the three metals recorded were found to be highly correlated, zinc levels tended to be closer to guideline thresholds for possible biological effects. Sediment nutrient concentrations in the harbour tended to decline with distance from the inner harbour and associated rivers. Te Puna Estuary showed comparatively high nitrogen and phosphorus loadings. Comparison of sediment nutrient concentrations with other New Zealand estuaries indicates that the Tauranga Harbour sits within a range typical for slightly to moderately enriched estuaries. Although total phosphorus was low compared with other estuaries, total N:P ratios suggest Tauranga Harbour is still limited by nitrogen. We developed a BHM using statistical ordination techniques to identify key stressors affecting the ‘health’ of macrofaunal communities. Sediments, nutrients and heavy metals were identified as key ‘stressors’, i.e. variables affecting the ecology of the harbour. Therefore, three multivariate models were developed based on the variability in community composition using canonical analysis of principal coordinates (CAP). The ecological assemblages generally reflected gradients of stress or pollution very well. However, the CAP models for sedimentation and contamination performed best. In general, the multivariate models were found to be more sensitive to changing ecological health than simple univariate measures (abundance, species diversity, evenness and Shannon-Wiener diversity). This finding has also been reported in the literature where univariate measures based on abundance and diversity were only able to detect significant differences between the most and least disturbed sites, but were not able to differentiate between smaller relative changes in ecological health. Hence univariate measures were less sensitive to smaller degradative changes in community composition. For Tauranga Harbour, ordination models based on community composition appear to be a more sensitive measure of ‘health’ along an ecological gradient and should enable long term degradative change from multiple disturbances to be assessed. This BHM approach can be used as a management or monitoring tool where sites are repeatedly sampled over time and tracked to determine whether the communities are moving towards a more healthy or unhealthy state. The key species at ‘healthy’ and ‘impacted’ sites as determined from the CAP models were also identified. Species at ‘impacted’ sites can be considered to be tolerant to the stressor (i.e. sediment, nutrients or contaminants), while species with high abundances at only ‘healthy’ sites are sensitive to increasing stressors. We developed species response models for 20 taxa. Although the type of response differed by taxa and stressor, variation in the abundance of most of the taxa modelled was most likely to be better predicted by sedimentation. Unimodal responses were almost always observed in response to nutrients, while declines or skewed unimodal responses were most often observed in response to sedimentation and metals. The results from this study are consistent with models of macrofaunal species occurrence with respect to sediment mud content developed across a range of New Zealand estuaries by Thrush et al. (2003). Within this report we extend this analysis by also developing models of macrofaunal species occurrence with respect to nutrient and contaminants loadings. Ultimately such statistical models provide a tool to forecast the distribution and abundance of species associated with habitat changes in sediments, nutrients and metals. In conclusion, Tauranga Harbour is a predominantly sandy harbour with slight to moderate enrichment and low levels of heavy metal contaminants. Sites identified as most impacted by elevated sediments, heavy metal contaminants and nutrients were generally located in the upper reaches of estuaries in some of the least exposed locations. To some extent, this reflects the natural progression of an estuary from land to sea; however, the rates of accumulation of sediments and nutrients have been accelerated as a result of anthropogenic land-based activities. Sediments and contaminants were found to explain the largest variance in benthic communities. Species response models suggest that taxa were either sensitive to elevated sediments, nutrients loading or contamination at all levels, or sensitive to these stressors beyond a critical point

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement

COMRADES determines in vivo RNA structures and interactions.

The structural flexibility of RNA underlies fundamental biological processes, but there are no methods for exploring the multiple conformations adopted by RNAs in vivo. We developed cross-linking of matched RNAs and deep sequencing (COMRADES) for in-depth RNA conformation capture, and a pipeline for the retrieval of RNA structural ensembles. Using COMRADES, we determined the architecture of the Zika virus RNA genome inside cells, and identified multiple site-specific interactions with human noncoding RNAs.This work was supported by Cancer Research UK (C13474/A18583, C6946/A14492) and the Wellcome Trust (104640/Z/14/Z, 092096/Z/10/Z) to E.A.M. O.Z. was supported by the Human Frontier Science Program (HFSP, LT000558/2015), the European Molecular Biology Organization (EMBO, ALTF1622-2014), and the Blavatnik Family Foundation postdoctoral fellowship. G.K. and M.G. were supported by Wellcome Trust grant 207507 and UK Medical Research Council. A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. 17197 to JCM). J.C.M was also supported by core funding from EMBL. I.G. and L.W.M. were supported by the Wellcome Trust Senior Fellowship in Basic Biomedical Science to I.G. (207498/Z/17/Z). I.J.M., L.F.G. and J.S.-G. were supported by grants R01GM104475 and R01GM115649 from NIGMS. C.K.K was supported by City University of Hong Kong Projects 9610363 and 7200520, Croucher Foundation Project 9500030 and Hong Kong RGC Projects 9048103 and 9054020. C.-F.Q. was supported by the NSFC Excellent Young Scientist Fund 81522025 and the Newton Advanced Fellowship from the Academy of Medical Sciences, UK

An architecture for flexible distributed experimentation and control with an AME 5000 plasma etcher

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 72-74).by Aaron E. Gower.M.S