Advances in Machine Condition Monitoring and Fault Diagnosis

In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

Statistical Methods for Semiconductor Manufacturing

In this thesis techniques for non-parametric modeling, machine learning, filtering and prediction and run-to-run control for semiconductor manufacturing are described. In particular, algorithms have been developed for two major applications area: - Virtual Metrology (VM) systems; - Predictive Maintenance (PdM) systems. Both technologies have proliferated in the past recent years in the semiconductor industries, called fabs, in order to increment productivity and decrease costs. VM systems aim of predicting quantities on the wafer, the main and basic product of the semiconductor industry, that may be physically measurable or not. These quantities are usually ’costly’ to be measured in economic or temporal terms: the prediction is based on process variables and/or logistic information on the production that, instead, are always available and that can be used for modeling without further costs. PdM systems, on the other hand, aim at predicting when a maintenance action has to be performed. This approach to maintenance management, based like VM on statistical methods and on the availability of process/logistic data, is in contrast with other classical approaches: - Run-to-Failure (R2F), where there are no interventions performed on the machine/process until a new breaking or specification violation happens in the production; - Preventive Maintenance (PvM), where the maintenances are scheduled in advance based on temporal intervals or on production iterations. Both aforementioned approaches are not optimal, because they do not assure that breakings and wasting of wafers will not happen and, in the case of PvM, they may lead to unnecessary maintenances without completely exploiting the lifetime of the machine or of the process. The main goal of this thesis is to prove through several applications and feasibility studies that the use of statistical modeling algorithms and control systems can improve the efficiency, yield and profits of a manufacturing environment like the semiconductor one, where lots of data are recorded and can be employed to build mathematical models. We present several original contributions, both in the form of applications and methods. The introduction of this thesis will be an overview on the semiconductor fabrication process: the most common practices on Advanced Process Control (APC) systems and the major issues for engineers and statisticians working in this area will be presented. Furthermore we will illustrate the methods and mathematical models used in the applications. We will then discuss in details the following applications: - A VM system for the estimation of the thickness deposited on the wafer by the Chemical Vapor Deposition (CVD) process, that exploits Fault Detection and Classification (FDC) data is presented. In this tool a new clustering algorithm based on Information Theory (IT) elements have been proposed. In addition, the Least Angle Regression (LARS) algorithm has been applied for the first time to VM problems. - A new VM module for multi-step (CVD, Etching and Litography) line is proposed, where Multi-Task Learning techniques have been employed. - A new Machine Learning algorithm based on Kernel Methods for the estimation of scalar outputs from time series inputs is illustrated. - Run-to-Run control algorithms that employ both the presence of physical measures and statistical ones (coming from a VM system) is shown; this tool is based on IT elements. - A PdM module based on filtering and prediction techniques (Kalman Filter, Monte Carlo methods) is developed for the prediction of maintenance interventions in the Epitaxy process. - A PdM system based on Elastic Nets for the maintenance predictions in Ion Implantation tool is described. Several of the aforementioned works have been developed in collaborations with major European semiconductor companies in the framework of the European project UE FP7 IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance); such collaborations will be specified during the thesis, underlying the practical aspects of the implementation of the proposed technologies in a real industrial environment

A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Chemical machining of advanced ceramics

Not until recently did we see an enormous surge of interest in the study of machining of advanced ceramics. This has resulted in significant advances lately in their development and usage. Machinable glass ceramics, boron nitride and silicon carbide are commonly used in the industry and their major features of attraction are their inherent properties. Previous studies on machining of these materials were mainly performed by other machining methods, such as electrode discharge machining, laser beam machining and abrasive jet machining. Although chemical machining is one of the oldest machining methods employed, the literature survey reveals a lack of knowledge in this particular aspect. Further understanding is required on the chemical machining characteristics of advanced ceramics as well as their performance and relationship between the variables and parameters involved in the process. Therefore, the aim of our study is to examine and establish the relationship between etching rate, surface roughness and dimensional accuracy with the relevant variables involved and at the same time to develop the predictive models for all outputs that we believe are beneficial to the manufacturing industries.A comprehensive review was written and published recently in a Journal on the current advanced ceramics machining techniques [1]. The chemical machining process was successfully conducted in this study with a variety of selected etchants. Using the RSM methodology the first and second order models were developed to study the chemical machining process and relationship between the outputs (etching rate, surface roughness and dimensional accuracy) with the selected variables, namely, etching temperature, etching duration, etchant and etchant’s concentration. A number of predictive models were developed followed by optimisation studies of chemical machining to obtain the best performance of chemical machining of advanced ceramics. Artificial neural network was also used as the analytical tool to evaluate the experimental data and validate the results generated by response surface roughness, and both results were found to be in good agreement with each other. Artificial neural network was performed by software of NeuroSolution 5.From the chemical etching studies both the etching temperature and etchant used have significant influence on the etch rate. Generally, the higher the etching temperature the greater the etch rates was observed for the substrates. The best etch rate was found in HBr etchant for MGC and BN, and the highest etch rate performance for SiC was found in H3PO4 etchant. For surface roughness, different substrates were found to be influenced by different variables. For MGC and BN, these substrates were affected by etching temperature and the best surface roughness occurred at high etching temperature of 90oC. Etching duration was also found to be critical in determining the quality of SiC surface roughness during chemical machining.Experimental data revealed that etching rate was closely correlated to surface roughness as well as the etching ratio. However, using the best etching rate it failed to yield the quality surface roughness, but produced the best etching ratio. Each variable presented different level of significance for each output of chemical machining. The results of etch rate and etch ratio also showed that etching temperature and etching duration imparted significant impact on the chemical machining of all substrates. In the analysis of surface roughness, etching temperature was found to be the critical variable in chemical machining of machinable glass ceramics. Etching temperature and etchant influenced the surface roughness of boron nitride whereas surface roughness of silicon carbide was more dependent on etching duration and etchant used.Predictive models were developed using DE 7 once the analysis of data was completed. A total of 27 predictive models were developed for each substrate and each output. This predictive model can be used directly in the industry with the selected substrate and etchant. Optimisation of chemical machining was also performed. For machinable glass ceramic, the optimum of chemical machining happened at 100oC in 10.5 molarity HCl etchant for 30 minutes. Results of chemical machining of machinable glass ceramics were obtained with optimal etching rate of 0.0008g/min, surface roughness improvement of 81.818nm (48% improvement) and etching ratio of 3.403. In chemical etching of boron nitride, the best result occurred at 40oC in 6 molarity HBr for 62 minutes. The etching rate obtained for BN is 0.00025g/min, with surface roughness improvement of 0.01nm (16% improvement) and etching ratio of 3.153. For the chemical etching of silicon carbide, the best performance occurred at 75oC in 8.5 molarity of HBr for 240 minutes. The optimal value of etching rate for silicon carbide is 0.0009g/min, with surface roughness improvement of 128.71um (35% improvement) and etching ratio of 10.004

Modeling of the MEMS Reactive Ion Etching Process Using Neural Networks

Abstract Reactive ion etch (RIE) is commonly used in microelectromechanical systems (MEMS) fabrication as plasma etching method, where ions react with wafer surface substrate in plasma environment. Due to the importance of RIE in the MEMS field, two prediction models are established to predict the wafer status in reactive ion etching process: back-propagation neural network (BPNN) and principle component analysis BPNN (PCABPNN). These models have the potential to reduce the overall cost of ownership of MEMS equipment by increasing the wafer yield, and not depend upon monitoring wafers or expensive metrology rather it will enable inexpensive real-time wafer-to-wafer control applications in RIE. The artificial neural net (ANN) is trained with historical available input-output process data. Once trained, the ANN forecasts the process output rapidly if given the input values

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number