Binning for IC Quality: Experimental Studies on the SEMATECH Data

The earlier smaller bipolar study did not provide a high enough bin 0 population to directly observe test escapes and thereby estimate defect levels for the best bin. Results presented here indicate that the best bin can be reasonably expected to show a 2 - 5 factor improvement in defect levels over the average for the lot for moderate to high yields (the overall yield for these experiments was approximately 65%). The experiments also confirm the dependence of the best bin quality on test transparency. The defect level improvement is poorer for the case Of IDDQ escapes where the tests applied had a much higher escape rate. Overall experimental results are consistent with analytical projections for typical values of the clustering parameter in [9]. The final version of this paper will include extensive analysis to validate the analytical models based on this data

High cycle fatigue life prediction of laser additive manufactured stainless steel:A machine learning approach

Variations in the high cycle fatigue response of laser powder bed fusion materials can be caused by the choice of processing and post-processing strategies. The numerous influencing factors arising from the process demand an effective and unified approach to fatigue property assessment. This work examines the use of a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L. A dataset, consisting of fatigue life data for samples subjected to varying processing conditions (laser power, scan speed and layer thickness), post-processing treatments (annealing and hot isostatic pressing) and cyclic stresses, was constructed for simulating a complex nonlinear input-output environment. The associated fracture mechanisms, including the modes of crack initiation and deformation, were characterised. Two models, by employing the processing/post-processing parameters and the static tensile properties respectively as the inputs, were developed from the training data. Despite the diverse fatigue and fracture properties, the models demonstrated good prediction accuracy when checked against the test data, and the computationally-derived fuzzy rules agree well with understanding of the fracture mechanisms. Direct application of the model to literature results, however, yielded a range of prediction accuracies because of the variability in the reported data. Retraining the model by incorporating the literature results into the dataset led to improved modelling performance.Accepted versio

Roles of Candida albicans Mig1 and Mig2 in glucose repression, pathogenicity traits, and SNF1 essentiality.

Metabolic adaptation is linked to the ability of the opportunistic pathogen Candida albicans to colonize and cause infection in diverse host tissues. One way that C. albicans controls its metabolism is through the glucose repression pathway, where expression of alternative carbon source utilization genes is repressed in the presence of its preferred carbon source, glucose. Here we carry out genetic and gene expression studies that identify transcription factors Mig1 and Mig2 as mediators of glucose repression in C. albicans. The well-studied Mig1/2 orthologs ScMig1/2 mediate glucose repression in the yeast Saccharomyces cerevisiae; our data argue that C. albicans Mig1/2 function similarly as repressors of alternative carbon source utilization genes. However, Mig1/2 functions have several distinctive features in C. albicans. First, Mig1 and Mig2 have more co-equal roles in gene regulation than their S. cerevisiae orthologs. Second, Mig1 is regulated at the level of protein accumulation, more akin to ScMig2 than ScMig1. Third, Mig1 and Mig2 are together required for a unique aspect of C. albicans biology, the expression of several pathogenicity traits. Such Mig1/2-dependent traits include the abilities to form hyphae and biofilm, tolerance of cell wall inhibitors, and ability to damage macrophage-like cells and human endothelial cells. Finally, Mig1 is required for a puzzling feature of C. albicans biology that is not shared with S. cerevisiae: the essentiality of the Snf1 protein kinase, a central eukaryotic carbon metabolism regulator. Our results integrate Mig1 and Mig2 into the C. albicans glucose repression pathway and illuminate connections among carbon control, pathogenicity, and Snf1 essentiality

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license

On Privacy and Utility while Improving Software Quality

Software development produces large amounts of data both from the process, as well as the usage of the software product. Software engineering data science turns this data into actionable insights for improving software quality. However, the processing of this data can raise privacy concerns for organizations, which are obligated by law, regulations and polices, to protect personal and business sensitive data. Early data privacy studies in sub-disciplines of software engineering found that applying privacy algorithms often degraded the usefulness of data. Hence, there is a recognized need for finding a balance between privacy and utility. A survey of data privacy solutions for software engineering data was conducted. Overall, researchers found that a combination of data minimization and obfuscation of data, produced results with high levels of privacy while allowing data to remain useful

THE MEASUREMENT OF POLITICAL INSTABILITY AND ITS LINK WITH MACROECONOMIC PERFORMANCE, FOOD SECURITY AND INCOME INEQUALITY

1noopenPolitical instability has long been at the centre of international debates in terms of its dimensions, reasons, and consequences. The issue of an unstable political environment is highly important due to its link with socio-economic problems that political instability brings to the people of a country. But before these connections are observed, the measurement of political instability should be correctly defined. Therefore, the first step of studies dealing with political instability should include a comprehensive explanation of what is meant by “political instability”, considering the possibility that different dimensions of political instability may have different consequences. In this context, this thesis claims that political instability cannot be fitted into a single mould and it has more than one dimension. When the crucial issue of how to measure political instability is settled, this thesis empirically investigates both the connections between political instability and macroeconomic performance and the nexus between political instability, food security and income inequality. The thesis starts with the Introduction part, which introduces the aim of the study and data and quantitative methods that will be exploited in the next chapters. In addition, this part also displays the general findings, main contribution to existence literature, constraints and future research. Chapter I, in which the dimensions of political instability is determined, is the cornerstone of the thesis, since the next two chapters employ these identifications of political instability. Principal Component Analysis (PCA), which is a dimensionality reduction method, is used as a tool to identify the measurement of political instability by using 11 political risk variables taken from the International Country Risk Guide dataset (The PRS Group 2014) observed on 117 countries. The results suggest that the first two principal components are selected and named as Structural Defect and Disorder of Polity Quality, respectively. Furthermore, Chapter I also shows how these two aspects of political instability are characterized by the following three government forms: Parliamentary System, Presidential System, Semi-Presidential System. In addition, Hierarchical Clustering by using Ward’s linkage algorithm is performed to divide countries into smaller clusters based on their similarities in terms of Structural Defect and Disorder of Polity Quality. Chapter II and Chapter III use panel Vector Autoregression Analysis (panel VAR) in generalized methods of moment (GMM) over the period of 2008-2017. While Chapter II analyzes the link between political instability and macroeconomic performance in the set of considered countries, Chapter III deals with the nexus between political instability, food security and income inequality. In both chapters, the results suggest that the direction and significance of these links sometimes change according to two different dimensions of political instability. That means that different aspects of political instability produce different results. Additionally, there is always an adverse relationship between two different aspects of political instability and other variables in the analysis. Furthermore, both Chapter II and Chapter III analyze the impulse response functions (IRFs) to better understand the reaction of variables to each other (aftershocks). Finally, these chapters further examine the forecast-error variance decompositions (FEVDs) to show the proportion of movements in the dependent variables that are due to their own shocks versus shocks to the other variables.L’instabilità politica é stata a lungo al centro dei dibattiti internazionali in termini di dimensioni,ragioni e conseguenze. La questione di un ambiente politico instabile riveste molta importanza per il suo legame con i problemi socio-economici che l'instabilità politica arreca alle persone di un paese. Ma prima che queste connessioni siano osservate, la misura dell'instabilità politica dovrebbe essere definita correttamente. Pertanto, la prima fase degli studi che si occupano di instabilità politica dovrebbe includere una spiegazione esauriente di cosa si intende per "instabilità politica", considerando la possibilità che diverse dimensioni dell'instabilità politica possano avere conseguenze diverse. In questo contesto, questa tesi si propone di approfondire il tema dell'instabilità politica partendo dall’idea che si tratti di un concetto complesso e multidimensionale. La tesi si propone, in primo luogo, di riuscire a misurare tale concetto individuandone le necessarie dimensioni ed indicatori che la caratterizzano. Dopo aver risolto la questione cruciale della misurazione dell'instabilità politica, la tesi propone un’analisi delle connessioni tra l’instabilità politica e la performance macroeconomica ma anche tra instabilità politica, sicurezza alimentare e disuguaglianza di reddito. La tesi inizia con la parte introduttiva, che introduce l'obiettivo dello studio e dati e metodi quantitativi che verranno utilizzati nei capitoli successivi. Inoltre, questa parte mostra anche i risultati generali, il contributo principale alla letteratura, i vincoli e la ricerca futura. Il Capitolo I, in cui si determinano le dimensioni dell'instabilità politica, è la pietra angolare della tesi, in quanto i due capitoli successivi impiegano i risultati ottenuti in tale capitolo. L’analisi delle Componenti Principali (ACP), che è un metodo di riduzione della dimensionalità, viene utilizzato come strumento per misurare l'instabilità politica utilizando 11 variabili di rischio politico tratte dal dataset della International Country Risk Guide (The PRS Group 2014) osservato in 117 paesi. I risultati suggeriscono che le l’instabilità politica debba essere declinata in due componenti, denominate rispettivamente come Il Difetto Strutturale e Il Disordine della Qualità Politica. Inoltre, il Capitolo I mostra anche come questi due aspetti dell'instabilità politica siano caratterizzati dalle seguenti tre forme di governo: Sistema Parlamentare, Sistema Presidenziale, Sistema Semi-Presidenziale. Inoltre, il Clustering Gerarchico, utilizzando l’algoritmo di collegamento di Ward, viene eseguito per dividere i paesi in gruppi omogenei rispetto alle componenti dell’instabilità precedentemente indivduate, Il Difetto Strutturale e Il Disordine della Qualità Politica. Il Capitolo II e il Capitolo III utilizzano la panel Vector Autoregression Analysis (panel VAR) nei generalized methods of moment (GMM) nel periodo 2008-2017. Mentre il Capitolo II analizza il legame tra instabilità politica e performance macroeconomica dei paesi considerati, il Capitolo III si occupa del nesso tra instabilità politica, sicurezza alimentare e disuguaglianza di reddito. In entrambi i capitoli, i risultati suggeriscono che la direzione e il significato di questi legami a volte cambiano in base alle due diverse dimensioni dell'instabilità politica. Questo significa che diversi aspetti dell'instabilità politica producono risultati diversi. Per di più, c'è sempre una relazione avversa tra i due diversi aspetti dell'instabilità politica e altre variabili nell'analisi. Inoltre, sia il Capitolo II che il Capitolo III analizzano la Funzione di Risposta Impulsiva (IRFs) per comprendere meglio la reazione delle variabili tra loro (scosse di assestamento). Infine, questi capitoli esaminano ulteriormente la Scomposizione della Varianza dell'errore di Previsione (FEVDs) per mostrare la proporzione dei movimenti nelle variabili dipendenti che sono dovuti ai propri shock rispetto agli shock delle altre variabili.openBEYHAN ZEYNEPBeyhan, Zeyne

Defect cluster recognition system for fabricated semiconductor wafers

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type)

Resonant Ta Doping for Enhanced Mobility in Transparent Conducting SnO2

Transparent conducting oxides (TCOs) are ubiquitous in modern consumer electronics. SnO2 is an earth abundant, cheaper alternative to In2O3 as a TCO. However, its performance in terms of mobilities and conductivities lags behind that of In2O3. On the basis of the recent discovery of mobility and conductivity enhancements in In2O3 from resonant dopants, we use a combination of state-of-the-art hybrid density functional theory calculations, high resolution photoelectron spectroscopy, and semiconductor statistics modeling to understand what is the optimal dopant to maximize performance of SnO2-based TCOs. We demonstrate that Ta is the optimal dopant for high performance SnO2, as it is a resonant dopant which is readily incorporated into SnO2 with the Ta 5d states sitting ∼1.4 eV above the conduction band minimum. Experimentally, the band edge electron effective mass of Ta doped SnO2 was shown to be 0.23m0, compared to 0.29m0 seen with conventional Sb doping, explaining its ability to yield higher mobilities and conductivities

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments