Query: Utilizing Single Scan and Enhanced Design-Based Binning Methodologies for Improved Process Window and Hotspot Discovery

Candidate papers:

abstract: Semiconductor yield improvement and stability is becoming increasingly more difficult to achieve with decreasing technology nodes. There are many new sources, types, and mechanisms of process induced systematic defects, with a growing demand to identify and control those sources of hotspots that impact yield with the most comprehensive results, fastest time, and lowest cost. Previously there has been extensive characterization and publications on the techniques used for hotspot discovery, with little overall improvement to the flow and time to results needed to keep up with today's fast paced development process which requires rapid results. We propose implementing new inspection and binning algorithms to the process window discovery methodology, to achieve improvements in time to results, process window qualification, and hotspot identification. These systematic defect discovery improvements enable lithography processes to be controlled and monitored more accurately and more precisely than ever before.
author_names: Sonal Singh

Shweta Khokale

Qian Xie

Panneerselvam Venkatachalam

Alexa Greer

Abhinav Mathur

Ankit Jain
title: Utilizing Single Scan and Enhanced Design Based Binning Methodologies for Improved Process Window and Hotspot Discovery
venue: 2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2019

abstract: Comparisons of human genomes show that more base pairs are altered as a result of structural variation including copy number variation than as a result of point mutations. Here we review advances and challenges in the discovery and genotyping of structural variation. The recent application of massively parallel sequencing methods has complemented microarray based methods and has led to an exponential increase in the discovery of smaller structural variation events. Some global discovery biases remain, but the integration of experimental and computational approaches is proving fruitful for accurate characterization of the copy, content and structure of variable regions. We argue that the long term goal should be routine, cost effective and high quality de novo assembly of human genomes to comprehensively assess all classes of structural variation.
author_names: Can Alkan

Bradley P Coe

Evan E Eichler
title: Genome structural variation discovery and genotyping
venue: Nature Reviews Genetics
year: 2011

abstract: The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms MAST, FIMO and GLAM2SCAN allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom) or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http:/meme.nbcr.net.
author_names: Timothy L Bailey

Mikael Boden

Fabian A Buske

Martin C Frith

Charles E Grant

Luca Clementi

Jingyuan Ren

Wilfred W Li

William Stafford Noble
title: MEME Suite: tools for motif discovery and searching
venue: Nucleic Acids Res.
year: 2009

abstract: Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify 'biodiversity hotspots' where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a 'silver bullet' strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.
author_names: Norman Myers

Russell A Mittermeier

Cristina Goettsch Mittermeier

Gustavo Fonseca

Jennifer M Kent
title: Biodiversity hotspots for conservation priorities
venue: Nature
year: 2000

abstract: The scan statistic is commonly used to test if a one dimensional point process is purely random, or if any clusters can be detected. Here it is simultaneously extended in three directions:(i) a spatial scan statistic for the detection of clusters in a multi dimensional point process is proposed, (ii) the area of the scanning window is allowed to vary, and (iii) the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro portional to some known function. The main interest is in detecting clusters not explained by the baseline process. These methods are illustrated on an epidemiological data set, but there are other potential areas of application as well.
author_names: Martin Kulldorff
title: A spatial scan statistic
year: 1997

abstract: By exploiting the extremely large effective cross sections {10}\ensuremath{ }17} {10}\ensuremath{ }16}\mathrm{cm}{2}\mathrm{molecule} available from surface enhanced Raman scattering (SERS) we achieved the first observation of single molecule Raman scattering. Measured spectra of a single crystal violet molecule in aqueous colloidal silver solution using one second collection time and about $2\ifmmode\times\else\texttimes\fi{{10}{5}\mathrm{W}\mathrm{cm}{2} nonresonant near infrared excitation show a clear `fingerprint' of its Raman features between 700 and $1700{\mathrm{cm}\ensuremath{ }1} Spectra observed in a time sequence for an average of 0.6 dye molecule in the probed volume exhibited the expected Poisson distribution for actually measuring 0, 1, 2, or 3 molecules.
author_names: Katrin Kneipp

Harald Kneipp

Lev T Perelman

Irving Itzkan

Ramachandra R Dasari

M S Feld
title: Single Molecule Detection Using Surface Enhanced Raman Scattering (SERS)
year: 1997

abstract: Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole genome, whole exome capture and multi sample low pass ~4x) 1000 Genomes Project datasets.
author_names: Mark A DePristo

Eric Banks

Ryan Poplin

Kiran V Garimella

Jared Maguire

Christopher Hartl

Anthony A Philippakis

Guillermo Del Angel

Manuel A Rivas

Matthew Hanna

Aaron McKenna

Tim Fennell

Andrew Kernytsky

Andrey Y Sivachenko

Kristian Cibulskis

S Gabriel

Dr David Altshuler

Mark J Daly
title: A framework for variation discovery and genotyping using next generation DNA sequencing data
venue: Nature Genetics
year: 2011

abstract: Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease association studies1 5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large scale functional datasets, and visualizing the resulting genome wide maps of chromatin state annotations.
author_names: Jason Ernst

Manolis Kellis
title: ChromHMM: automating chromatin state discovery and characterization
venue: Nature Methods
year: 2012

abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction) Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
author_names: Todd R Golub

Donna K Slonim

Pablo Tamayo

Christine Huard

Michelle Gaasenbeek

Jill P Mesirov

Hilary A Coller

Mignon L Loh

James R Downing

Michael A Caligiuri

Clara D Bloomfield

Eric S Lander
title: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
venue: Science
year: 1999

abstract: Summary: A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position specific frequency matrix. Results: We describe Find Individual Motif Occurrences (FIMO) a software tool for scanning DNA or protein sequences with motifs described as position specific scoring matrices. The program computes a log likelihood ratio score for each position in a given sequence database, uses established dynamic programming methods to convert this score to a P value and then applies false discovery rate analysis to estimate a q value for each position in the given sequence. FIMO provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats. The program is efficient, allowing for the scanning of DNA sequences at a rate of 3.5 Mb/s on a single CPU. Availability and Implementation: FIMO is part of the MEME Suite software toolkit. A web server and source code are available at http:/meme.sdsc.edu. Contact: t.bailey@imb.uq.edu.au; t.bailey@imb.uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
author_names: Charles E Grant

Timothy L Bailey

William Stafford Noble
title: FIMO: scanning for occurrences of a given motif
venue: Bioinform.
year: 2011