Journal
ANNALS OF OPERATIONS RESEARCH
Volume 99, Issue -, Pages 373-383Publisher
BALTZER SCI PUBL BV
DOI: 10.1023/A:1019288220413
Keywords
yield maximization; design optimization; asymmetrical PDF
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This paper presents a general method for maximizing manufacturing yield when the realizations of system components are independent random variables with arbitrary distributions. Design specifications define a feasible region which, in the nonlinear case, is linearized using a first-order approximation. The method attempts to place the given tolerance hypercube of the uncertain parameters such that the area with higher yield lies in the feasible region. The yield is estimated by using the joint cumulative density function over the portion of the tolerance hypercube that is contained in the feasible region. A double-bounded density function is used to approximate various bounded distributions for which optimal designs are demonstrated on a tutorial example. Monte Carlo simulation is used to evaluate the actual yields of optimal designs.
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