Dependability of complex semiconductor systems: Learning Bayesian networks for decision support

Author

Bouaziz, Mohammed Farouk ; Zamai, Eric ; Duvivier, Frederic ; Hubac, Stéphane

Author_Institution

G-SCOP, CNRS, Grenoble, France

fYear

2011

fDate

15-17 June 2011

Firstpage

7

Lastpage

12

Abstract

The production of microelectronic components is characterized by an important complexity of production context, high technology renewal cadence, a strong customer requirement and an uncertain environment. This has an impact on the cycle time, costs and equipment efficiencies. This paper presents a general methodology to manage the risks of complex semiconductors systems. A literature review about process control, risk analysis methods and Bayesian networks is presented. A first structure of the predictive behavior model is proposed, this model is based on Bayesian learning methods.

Keywords

belief networks; control engineering computing; decision support systems; learning (artificial intelligence); process control; production engineering computing; risk analysis; semiconductor device manufacture; Bayesian learning methods; Bayesian networks; complex semiconductor system dependability; costs efficiencies; customer requirement; cycle time; decision support; equipment efficiencies; microelectronic component production; predictive behavior model; process control; production context complexity; renewal cadence; risk analysis methods; Bayesian methods; Maintenance engineering; Manufacturing; Metrology; Predictive models; Production; Risk analysis; Bayesian networks; complex semiconductor systems; decision support; dependability; learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Dependable Control of Discrete Systems (DCDS), 2011 3rd International Workshop on

Conference_Location

Saarbrucken

Print_ISBN

978-1-4244-8969-5

Type

conf

DOI

10.1109/DCDS.2011.5970310

Filename

5970310