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

Abstract

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 a 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.