Circuit Tolerance Design Using an Improved Genetic Algorithm

In this paper, we present an improved genetic algorithm to solve the worst-case circuit tolerance design problem, which has many design parameters and constraints. The evolutionary design approach, which is called a quality-engineering-based genetic algorithm (QEGA), with a penalty function is proposed for solving the constrained optimization problem. The QEGA approach is able to explore a wide design parameter space without any prior knowledge about position and size of the region of acceptability. The QEGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the quality engineering method, which can exploit the optimum offspring. The quality engineering method is inserted between crossover and mutation operations of the TGA. Then, the systematic reasoning ability of the quality engineering method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the QEGA approach can be more robust and quickly convergent. In the worst-case circuit tolerance design problem, the vertex analysis has been used to check the feasibility of any candidate tolerance region. For this reason, if, as for a large part of cases, the region of acceptability is convex and simply connected, the QEGA approach ensures an optimal design with 100% yield. The proposed QEGA approach with a penalty function is effectively applied to solve the worst-case circuit tolerance design problem. The computational experiments also show that the presented QEGA approach can obtain better results than previous design methods