| Summary: | 碩士 === 中原大學 === 機械工程研究所 === 96 === Spin coating has been widely used in big domestic factories such as the advanced optical-disc manufacturers. The manufacturers apply spin coating to dye coating and bonding coating for two main advantages, namely the high uniformity and the low cost. With spin coating, engineers will only need to properly adjust the coating parameters, acceleration time (ACC Time), step time and spin speed to complete the discs’ coating process. Nevertheless, optimizing the mentioned parameters is involved with the use of the trial-and-error method by the engineers during the recipe search. Although the trial-and-error method might search out the optimal recipe, it will, however, heavily rely on empirical rules. To an experienced engineer, he might be able to quickly search out an optimal recipe, but to a less experienced engineer, it will take him more time and cost to reach the goal of recipe search. Hence, the trial-and-error method usually does not render economical efficiency.
In this thesis, we first propose applying a radial basis function network (RBFN) to a series of preplanned experimental data, in establishing a model, and adopting an intelligent-type Taguchi genetic algorithm (TGA) to evolve the optimal parameters. Next, we substitute the parameters into the process model for verification. If the model conforms to the set conditions, the optimal recipe search is then completed. Through our experimental verification, we observe that the proposed method not only can effectively search out the optimal recipe but also renders a goal conforming to the discrepancy range of (±0.003) set by the process. In comparison, the results obtained from the proposed method come quite close to that of the trial-and-error method; furthermore, the proposed method can greatly reduce the recipe search time. When the production process target alters, engineers equipped with the proposed method, unlike with the other method, will be able to reach the optimal recipe search goal using only a small amount of experimental planning.
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