Stammdaten

Optimal design of experiments for semiconductor lifetime data
Untertitel:
Kurzfassung:

Performing experiments is necessary to find influences of different factors on the measured output. In semiconductor industry experiments are mainly performed following predefined specifications and guidelines, given by experts for the device under test (DUT). The statistical method design of experiments (DoE) provides an objective solution to the question: which experiments have to be performed to get the most information concerning main influencing factors and interaction between factors. In practical usage classical DoE often reach their limits, especially when resources for experiments are meagre. A remedy is given by optimal DoEs. They are more flexible and offer the possibility to optimize e.g. the prediction accuracy on a pre-defined area, where performing measurements is difficult. For this purpose the IV-optimality criterion is used in this paper. On the basis of already performed experiments, an exchange algorithm proposed by Spoöck and Pilz [2] was used to select 3 further desired experiments. After their performance they were evaluated and, as expected, an improvement in the mean squared error of prediction (MSEP) was observed.

Schlagworte:
Publikationstyp: Beitrag in Sammelwerk (Autorenschaft)
Art der Veröffentlichung Printversion
Erschienen in: Proceeding on the 9th International Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)
Proceeding on the 9th International Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)
zur Publikation
 ( )
Erscheinungdatum: 11.2013
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Seite: S. 285 - 288

Identifikatoren

ISBN:
  • 9781467345798
ISSN: -
DOI: -
AC-Nummer: -
Homepage:
Open Access
  • Kein Open-Access

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Statistik
Universitätsstraße 65-67
9020  Klagenfurt am Wörthersee
Österreich
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 101029 - Mathematische Statistik
Forschungscluster
  • Nachhaltigkeit
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: n.a.)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen Keine Arbeitsgruppe ausgewählt

Kooperationen

Keine Kooperationspartner ausgewählt

Beiträge der Publikation

Keine verknüpften Publikationen vorhanden