Two-phased meta-heuristic methods for the post-mapping yield control problem

Yield control plays an important role in the TFT-LCD manufacturing firms, and the post-mapping operation is a crucial step. The post-mapping operation combines one TFT plate and one CF plate to form an LCD. Each TFT and CF plate is divided into a number of panels. The LCD panel is acceptable only when both TFT and CF panels are good. The TFT-LCD manufacturing firms use the sorter, a kind of robot, to increase the yield for matching TFT and CF plates. Evidently, there will be a great loss if a random mapping policy is executed. In this study, we first apply two of the most popular meta-heuristic methods to solve the post-mapping problem: Genetic Algorithm (GA) and Simulated Annealing (SA). However, when the number of matched cassettes is large, the number of ways for choosing different matched objects will become so enormous that the initial population in GA (or initial solution in SA) should be selected with a proper procedure. That is, we propose a two-phased GA and SA to improve the performance of the initial population. The basic concept of phase one is to generate an efficient initial population (or initial solution). In phase one, the initial population is created based on the optimal solution to the cassette-matching problem. In phase two, we perform GA (or SA) with the initial population created in phase one. The four different heuristic algorithms are tested for the same data to compare the various ports in the post-mapping yield control problem. The result shows that proposed two-phased algorithms provide a more excellent solution than GA and SA.