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Off‐line quality control via yield constrained variability minimization in circuit design
Author(s) -
Ilumoka A. A.
Publication year - 1991
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.4680070511
Subject(s) - reliability engineering , consistency (knowledge bases) , reliability (semiconductor) , constraint (computer aided design) , monte carlo method , minification , computer science , curse of dimensionality , quality (philosophy) , product (mathematics) , mathematical optimization , engineering , power (physics) , mathematics , statistics , mechanical engineering , physics , philosophy , geometry , epistemology , quantum mechanics , artificial intelligence , machine learning , programming language
During the lifetime of any system, e.g. an electronic circuit, sources of variation of parameters include fabrication, operational and environmental (FOE) variables. Since these sources of variation are not under designer control, one important objective at the product design stage is to reduce rather than control their influence. The aim of this paper is to present a methodology whereby settings of system parameters which make product performance less sensitive to FOE variations are identified. By so doing, reliability (consistency of acceptable performance), and hence quality, is enhanced. The approach taken is to minimize performance variability subject to a constraint on yield. This objective ensures consistency of performance while the constraint ensures acceptability of performance. Multiple performances are handled via a weighted sum objective. The Monte Carlo approach ensures that any parameter probability density function is handled and that computational cost does not increase with system dimensionality. The effectiveness of the technique is illustrated via a practical system example—an electronic circuit having 11 design parameters.