Efficient yield optimization with limited gradient information

Abstract

In this work an efficient strategy for yield optimization with uncertain and deterministic optimization variables is presented. The gradient based adaptive Newton-Monte Carlo method is modified, such that it can handle variables with (uncertain parameters) and without (deterministic parameters) analytical gradient information. This mixed strategy is numerically compared to derivative free approaches.

Multi-Objective Yield Optimization for Electrical Machines using Machine Learning

“…Classify p (i) ∈ Ω x (accepted) end if 17: end for the yield, i.e., the maximization of the probability that one realization in a manufacturing process fulfills all performance feature specifications. As optimization variables, the uncertain design parameters can be considered as for example in [29], or both, uncertain and deterministic design parameters can be considered as in [30]. In this work we will focus on deterministic design parameters as optimization variables, i.e., the optimization problem reads…”