A Symbolic-Numeric Approach to Multi-Objective optimization in manufacturing design
Some important classes of optimization problems originating from the optimal design of semiconductor memories such as SRAM, aiming at boosting the yield rate, are studied. New optimization methods for the classes based on a symbolic algorithm called quantifier elimination, combined with numerical computation, are proposed. The total efficiency of the design process is improved by reducing the number of numerical yield-rate evaluations. In addition, useful information such as the explicit relations among design variables, objective functions, and the yield rate, is provided. Since genetic algorithms are the most popular type of EA, there exist many implementations.