The use of stochastic analytic center for yield maximization of systems with general distributions of component values

This paper presents a new method for maximizing manufacturing yield when the realizations of system components are dependent random variables with general distributions. The method uses a new concept of stochastic analytic center introduced herein to design the unknown parameters of component values. Design specifications define a feasible region which, in the nonlinear case, is linearized using a first-order approximation. The resulting problem becomes a convex optimization problem. Monte Carlo simulation is used to evaluate the actual yield of the optimal designs of a tutorial example.