ENBIS Spring Meeting 2017

28 – 30 May 2017; Monastery of Schlägl in Upper Austria Abstract submission: 11 November 2016 – 5 March 2017

My abstracts


The following abstracts have been accepted for this event:

  • Assessing the Quality of Wafer Images

    Authors: Sarah Karasek (Graz University of Technology), Herwig Friedl (Graz University of Technology), Peter Scheibelhofer (AMS Austria Microsystems AG)
    Primary area of focus / application: Process
    Secondary area of focus / application: Quality
    Keywords: Process, Quality, Semi-conductors, Wafers
    Submitted at 9-Mar-2017 17:28 by Sarah Karasek
    29-May-2017 16:55 Assessing the Quality of Wafer Images
    In the semiconductor fabrication frequent quality checks during wafer production are essential. For the quality assessment of the so-called wafer bond process, one can take a look at the grayscale image of a wafer, which looks like a black-and-white photograph. Before the quality of the wafer can be judged by analyzing the grayscale image, it is important to know if the image contrast of the picture is high enough, because only then a reliable assessment is possible.
    This can be done by considering the grayscale frequencies. The corresponding histogram shows up a clear multi-modal behaviour. That is why we assume a finite mixture distribution with component specific parameters to analyze the data. In some cases, a mixture of Gaussians works very well, whereas sometimes a mixture of Gammas performs even better. In order to find the maximum likelihood estimates of the location and scale or shape parameters we utilize the well-known EM algorithm.
    Once we have estimates of the distribution of a single image, we are interested in how the image models vary over time during the production process. Thus it is planned to develop a routine which automatically detects relevantchanges in the image contrast.
  • Using Decision Theory and Value of Information for Assessing Process Improvement Actions

    Authors: Shawn Capser (Praxis Reliability Consulting)
    Primary area of focus / application: Process
    Secondary area of focus / application: Six Sigma
    Keywords: Decision theory, Value of information, Expected utility, Bayesian inference, Copulas, Continuous improvement , Process improvement
    Submitted at 20-Mar-2017 13:17 by Shawn Capser
    Process optimization occurs through a series of decisions that are a standard part of any continuous improvement process. Decisions are made anywhere within the supply chain and can target all elements of the assembly and manufacturing processes. The effectiveness of these decisions is based on the information available to the decision maker. Decision analysis provides a structured approach for quantifying the value of information that may be provided to the decision maker. This paper presents a process for determining the value of information that can be gained by evaluating linearly correlated continuous improvement actions. A unique approach to the application of Bayesian Inference is used to provide simulated estimates in the expected utility with increasing observations sizes. The results provide insight into the optimum observation size that maximizes the expected utility when assessing correlated process improvement initiatives, where such an example may be related to the evaluation of process layouts for Lean Six Sigma initiatives.