ENBIS-16 in Sheffield11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016
The following abstracts have been accepted for this event:
A New Control Chart for Monitoring the Parameters of a Zero-Inflated Poisson Process
Authors: Athanasios Rakitzis (University of Aegean), Amitava Mukherjee (XLRI-Xavier School of Management)
Primary area of focus / application: Process
Secondary area of focus / application: Process
Keywords: Average run length, Maximum likelihood estimation, Statistical Process Control, Wald statistic, Zero-inflated Poisson distribution
Submitted at 18-Apr-2016 15:07 by Athanasios Rakitzis
Random Number Generation for a Survival Bivariate Weibull Distribution
Authors: Mario César Jaramillo Elorza (Universidad Nacional de Colombia sede Medellín), Osnamir Elias Bru Cordero (Universidad Nacional de Colombia), Sergio Yañez Canal (Universidad Nacional de Colombia)
Primary area of focus / application: Modelling
Keywords: Bivariate Weibull, Gumbel-Hougaard copula, survival copula, CD-vines
Submitted at 20-Apr-2016 21:17 by Mario César Jaramillo Elorza
ISO 13053 and ISO 18404 - Have You Read these Standards?
Authors: Jonathan Smyth-Renshaw (Jonathan Smyth-Renshaw & Associates Ltd)
Primary area of focus / application: Business
Keywords: ISO 13053, ISO 18404, TPM, Statistics in business
Submitted at 21-Apr-2016 00:00 by Jonathan Smyth-Renshaw
QFD: An Effective Approach to Identify Factors for Service Simulation Experiments
Authors: Shuki Dror (ORT Braude College)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Business
Keywords: QFD, DOE, Service, Simulation
Submitted at 24-Apr-2016 18:00 by Shuki Dror
Clustering Variables Based on a Dynamic Mixed Criteria: Application to the Energy Management
Authors: Christian Derquenne (EDF R&D)
Primary area of focus / application: Mining
Secondary area of focus / application: Business
Keywords: Variables clustering, Correlation, Unidimensionality, Unsupervised learning, Time series, Energy management
Submitted at 25-Apr-2016 10:44 by Christian Derquenne
Failure Probability Estimation for Semiconductor Burn-In Studies Considering Synergies between Different Chip Technologies
Authors: Daniel Kurz (Department of Statistics, Alpen-Adria University of Klagenfurt), Horst Lewitschnig (Infineon Technologies Austria AG), Jürgen Pilz (Department of Statistics, Alpen-Adria University of Klagenfurt)
Primary area of focus / application: Reliability
Secondary area of focus / application: Quality
Keywords: Bayes, Binomial distribution, Burn-in, Clopper-Pearson, Serial system reliability
Submitted at 26-Apr-2016 10:48 by Daniel Kurz
To reduce the efforts associated with BI, semiconductor manufacturers aim at evaluating the failure probability p of the devices in their early life. This is achieved by means of a BI study, in which a large random sample of devices (>100k) is investigated for early failures after the BI. Based on the number of relevant failures, an upper bound for p can then be assessed, typically by using the exact Clopper-Pearson approach. As soon as this upper bound is below the predefined ppm-target, BI can be released.
In this talk, we show how to improve the estimation of early life failure probabilities for semiconductor BI studies by considering synergies (e.g. comparable chip layers) between different chip technologies. In other words, we partition the devices into disjunctive subsets and take into account additional information for the subsets being available from BI studies of related technologies.
From a statistical point of view, this requires deriving an upper bound for p from binomial subset data. To be consistent with the exact Clopper-Pearson approach, we i) compute the probability distribution for the number of failed devices, which might be randomly assembled from the failed subsets, and ii) infer the upper bound for p from a beta mixture distribution. Following a Bayesian approach, however, the upper bound for p is derived from the posterior distribution of p under negative-log-gamma prior distributions for the subset failure probabilities (in order to obtain a uniform prior distribution for p).
Finally, we show that, by considering synergies to already tested technologies, the total sample size of BI studies can be essentially reduced, especially in case of failures. Moreover, we indicate how we can further reduce the efforts of BI studies by additionally taking account of countermeasures implemented in the chip production process.
The work has been performed in the project EPT300, co-funded by grants from Austria, Germany, Italy, The Netherlands and the ENIAC Joint Undertaking. This project is co-funded within the programme "Forschung, Innovation und Technologie für Informationstechnologie" by the Austrian Ministry for Transport, Innovation and Technology.