The Development of an Immersive Virtual Reality Environment for Manufacturing and Improving Organizational Learning

This paper examines the applicability of an immersive virtual reality (VR) system to the process of organizational learning in a manufacturing context. The work focuses on the extent to which realism has to be represented in a simulated product build scenario in order to give the user an effective learning experience for an assembly task. Current technologies allow the visualization and manipulation of objects in VR systems but physical behaviors such as contact between objects and the effects of gravity are not commonly represented in off the shelf simulation solutions and the computational power required to facilitate these functions remains a challenge. This work demonstrates how physical behaviors can be coded and represented through the development of more effective mechanisms for the computer aided design (CAD) and VR interface.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

METAMORPHOSE VI – the Virtual Institute for artificial electromagnetic materials and metamaterials: Origin, mission, and activities

In this paper, we outline the background, mission, and activities of the Virtual Institute for Artificial Electromagnetic Materials and Metamaterials (METAMORPHOSE VI). This international association, founded in the framework of the FP-6 Network of Excellence METAMORPHOSE, aims at promoting and developing research, training, and dissemination activities in the emerging and highly dynamic field of advanced electromagnetic materials and metamaterials at both European and International levels. More than 300 researchers are currently associated with the METAMORPHOSE VI which networks them together in a learnt society. After a brief description of the association and its mission, we present an overview of the activities developed by the METAMORPHOSE VI, with a particular emphasis on the coordination of the European Doctoral Program on Metamaterials (EUPROMETA) and the organization of the International Congress on Advanced Electromagnetic Materials in Microwaves and Optics – metamaterials congress.

A critical ethnography of communication processes involving the management of oral chemotherapeutic agents by patients with a primary diagnosis of colorectal cancer: study protocol

Aim
To describe the protocol used to examine the processes of communication between health professionals, patients and informal carers during the management of oral chemotherapeutic medicines to identify factors that promote or inhibit medicine concordance.

Background
Ideally communication practices about oral medicines should incorporate shared decision-making, two-way dialogue and an equality of role between practitioner and patient. While there is evidence that healthcare professionals are adopting these concordant elements in general practice there are still some patients who have a passive role during consultations. Considering oral chemotherapeutic medications, there is a paucity of research about communication practices which is surprising given the high risk of toxicity associated with chemotherapy.

Design
A critical ethnographic design will be used, incorporating non-participant observations, individual semi-structured and focus-group interviews as several collecting methods.

Methods
Observations will be carried out on the interactions between healthcare professionals (physicians, nurses and pharmacists) and patients in the outpatient departments where prescriptions are explained and supplied and on follow-up consultations where treatment regimens are monitored. Interviews will be conducted with patients and their informal carers. Focus-groups will be carried out with healthcare professionals at the conclusion of the study. These several will be analysed using thematic analysis. This research is funded by the Department for Employment and Learning in Northern Ireland (Awarded February 2012).

Discussion
Dissemination of these findings will contribute to the understanding of issues involved when communicating with people about oral chemotherapy. It is anticipated that findings will inform education, practice and policy.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Playing free: an ethnography of Brazilian free improvisers

In this article I investigate the practice of free music improvisation in Brazil. The reflections and findings presented here are derived from research conducted as part of a four months Higher Education Academy (HEA, UK) Fellowship, carried out between February and June 2014. The aim was to enquire whether or how the practice of free improvisation is taught in the Brazilian higher education system.
As part of this ethnographic study visits to the following universities were scheduled:
The Federal University of Rio de Janeiro - UFRJ
The Universidade Federal do Estado do Rio de Janeiro (UNIRIO)
The University of São Paulo - USP
The Federal University of Minas Gerais – UFMG
The Federal University of Bahia – UFBA.
The Federal University of Rio Grande do Norte in Natal (UFRN) and
The ELM, the Escola Livre de Música in Unicamp.

I discuss here some general background thinking to the research process, specifically recalling the work of French composer and educator Alain Savouret. I proceed to examine the improvisational spirit, the improvisatory worldmaking approach (the ‘jeitinho brasileiro’) that is often considered to be integral to the Brazilian way of life. In the final part of the article I discuss applied ethnographic methodologies, including the design of questions that were used for over 50 video interviews with Brazilian musicians during the research. I conclude with a final reflection on the video interviews with a specific focus on whether free improvisation can be taught, and the importance of listening in the context of free improvisation practices.