Feature analysis and classification of manufacturing signatures based on semiconductor wafermaps

Automated tools for semiconductor wafer defect analysis are becoming more necessary as device densities and wafer sizes continue to increase. Trends towards larger wafer formats and smaller critical dimensions have caused an exponential increase in the volume of defect data which must be analyzed and stored. To accommodate these changing factors, automatic analysis tools are required that can efficiently and robustly process the increasing amounts of data, and thus quickly characterize manufacturing processes and accelerate yield learning. During the first year of this cooperative research project between SEMATECH and the Oak Ridge National Laboratory, a robust methodology for segmenting signature events prior to feature analysis and classification was developed. Based on the results of this segmentation procedure, a feature measurement strategy has been designed based on interviews with process engineers coupled with the analysis of approximately 1500 electronic wafermap files. In this paper, the authors represent an automated procedure to rank and select relevant features for use with a fuzzy pair-wise classifier and give examples of the efficacy of the approach taken. Results of the feature selection process are given for two uniquely different types of class data to demonstrate a general improvement in classifier performance

Automatic classification of spatial signatures on semiconductor wafermaps

This paper describes Spatial Signature Analysis (SSA), a cooperative research project between SEMATECH and Oak Ridge National Laboratory for automatically analyzing and reducing semiconductor wafermap defect data to useful information. Trends toward larger wafer formats and smaller critical dimensions have caused an exponential increase in the volume of visual and parametric defect data which must be analyzed and stored, therefore necessitating the development of automated tools for wafer defect analysis. Contamination particles that did not create problems with 1 micron design rules can now be categorized as killer defects. SSA is an automated wafermap analysis procedure which performs a sophisticated defect clustering and signature classification of electronic wafermaps. This procedure has been realized in a software system that contains a signature classifier that is user-trainable. Known examples of historically problematic process signatures are added to a training database for the classifier. Once a suitable training set has been established, the software can automatically segment and classify multiple signatures form a standard electronic wafermap file into user-defined categories. It is anticipated that successful integration of this technology with other wafer monitoring strategies will result in reduced time-to-discovery and ultimately improved product yield

Acoustic sequences in non-human animals: a tutorial review and prospectus.

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.This review was developed at an investigative workshop, “Analyzing Animal Vocal Communication Sequences” that took place on October 21–23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University of Tennessee, Knoxville. In addition to the authors, Vincent Janik participated in the workshop. D.T.B.’s research is currently supported by NSF DEB-1119660. M.A.B.’s research is currently supported by NSF IOS-0842759 and NIH R01DC009582. M.A.R.’s research is supported by ONR N0001411IP20086 and NOPP (ONR/BOEM) N00014-11-1-0697. S.L.DeR.’s research is supported by the U.S. Office of Naval Research. R.F.-i-C.’s research was supported by the grant BASMATI (TIN2011-27479-C04-03) from the Spanish Ministry of Science and Innovation. E.C.G.’s research is currently supported by a National Research Council postdoctoral fellowship. E.E.V.’s research is supported by CONACYT, Mexico, award number I010/214/2012.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/brv.1216

Quantitative temporal viromics: an approach to investigate host-pathogen interaction

A systematic quantitative analysis of temporal changes in host and viral proteins throughout the course of a productive infection could provide dynamic insights into virus-host interaction. We developed a proteomic technique called “quantitative temporal viromics” (QTV), which employs multiplexed tandem-mass-tag-based mass spectrometry. Human cytomegalovirus (HCMV) is not only an important pathogen but a paradigm of viral immune evasion. QTV detailed how HCMV orchestrates the expression of >8,000 cellular proteins, including 1,200 cell-surface proteins to manipulate signaling pathways and counterintrinsic, innate, and adaptive immune defenses. QTV predicted natural killer and T cell ligands, as well as 29 viral proteins present at the cell surface, potential therapeutic targets. Temporal profiles of >80% of HCMV canonical genes and 14 noncanonical HCMV open reading frames were defined. QTV is a powerful method that can yield important insights into viral infection and is applicable to any virus with a robust in vitro model

Elliptical local vessel density: a fast and robust quality metric for retinal images

A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patient's vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques

Validation of Microaneurysm-based Diabetic Retinopathy Screening across Retina Fundus Datasets

Abstract—In recent years, automated retina image analysis (ARIA) algorithms have received increasing interest by the medical imaging analysis community. Particular attention has been given to techniques able to automate the pre-screening of Diabetic Retinopathy (DR) using inexpensive retina fundus cameras. With the growing number of diabetics worldwide, these techniques have the potential benefits of broad-based, inexpensive screening. The contribution of this paper is twofold: first, we propose a straightforward pipeline from microaneurysm (an early sign of DR) detection to automatic classification of DR without employing any additional features; then, we quantify the generalisation ability of the MA detection method by employing synthetic examples and, more importantly, we experiment with two public datasets which consist of more than 1,350 images graded as normal or showing signs of DR. With cross-datasets tests, we obtained results better or comparable to other recent methods. Since our experiments are performed only on publicly available datasets, our results are directly comparable with those of other research groups. I