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]

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