The Model of Cycle Time Estimation for the Wafer Fabrication.
|Keywords:||晶圓製造;等候理論;生產週期時間;在製品量;批次機台;wafer fabrication;queueing theory;cycle time;WIP;batch workstation|
|Abstract:||晶圓製造廠所追求的生產目標不外乎是高產出率、短生產週期時間、高良率及準時交貨等，而上述目標均與生產週期時間有關，故生產週期時間的掌握在晶圓製造廠中佔有舉足輕重的角色。有鑑於此，本研究應用區塊基礎式週期時間演算法(Block Based Cycle Time Forecasting Algorithm; BBCT)推估產品在各工作站之生產週期時間。
The production target for the wafer fabrication includes high throughput、short cycle time、high yield and on time delivery,…, etc. All these targets are related with the cycle time. Precisely and quickly estimating the length of cycle times for each product type and the flow time for each workstation are important. In this study, the model for estimating the cycle time for each product type going through each workstation is developed based on the block based cycle time estimating algorithm (BBCT). To calculate the product's cycle time, the BBCT considers two factors：the variety of batch sizes on workstations and the difference in throughput rates among workstations. With such an idea, the average waiting time for a product waiting at a workstation includes (1) the waiting time resulted from the workload on the workstation, which is determined with the M/M/s queueing model and (2) the waiting time related to the batch forming, which is determined by the BBCT. However, the lot releasing from a workstation often causes the peak workload on the downstream serial workstations. The waiting time caused because of the peak workload (also named the second waiting time) will be longer than the waiting time derived with the average workload. Hence, when deriving the cycle time for a workstation, both the second waiting time resulting from the lots flowing from batch workstation to serial workstation and the batch forming time resulting from the lots flowing from serial workstation to batch workstation are considered. With the workstation's cycle time, Little's law is applied to calculate the workstation's WIP level. The case study shows that there only exists about 6% difference in average between the workstation flow time to the simulation results. Meanwhile, the calculation time of this model takes only a few minutes instead of a few hours for the simulation model. Result also shows that estimated the workstation WIP level is different from the simulation result about 0.2 lots in average only. Thus, the model developed for the cycle time estimation can quickly provide the good enough reference digits to production planning and production activity control system.
|Appears in Collections:||Thesis|