The design of Capacity Requirement Planning Model for the Photolithography Operations with Machine Dedication Consideration
|關鍵字:||產能需求規劃;垂直鎖定機台;工單指派;網路基礎指派;Capacity Requirement Planning;Machine dedication;job assignement;COLA;Network based assignment|
本文所提之黃光區產能需求分配模式，考量垂直鎖定機台的條件，係應用Chung and Huang  COLA演算法，求取分配至機台的製程規格負荷量，以便依各機台之製程規格負荷量進行工單之機台指派。接著，本文運用 Sule  氏之網路基礎指派法，來分派工單進行機台之指派。其以工單製程規格彈性之大小來決定工單選取順序，並以機台製程規格彈性的大小來決定機台指派之優先順序。待所有工單分配完畢後，即可求得工單指派至機台的結果與各機台利用率。
Owing to the requirement to improve process technology, there is a restriction with the process capability on line width for the photolithography operation in the semiconductor fabrication. In order to meet the specification of line dimension, jobs are required be processed on some machines for capability limitation. Also, there has machine dedication restriction for all the photolithography operations of critical layers. To solve these problems, this thesis develops a Capacity Requirement Planning model to get load balance with process capability and machine dedication considerations and then to do job assignment based on the optimized result of loading balance. This paper adopts “Capability-Oriented Loading Allocation”  to allocate the loading to each machine with machine capability and dedication restrictions in photolithography operations. Then, based on results of load allocation, job assignment method “Network based assignment” by Sule  is applied to do job assignment. Job priority will be determined according to job flexibility, ranking by ascending order. Then, machine selection is performed based on the machine flexibility index. The one has minimum machine flexibility index will have the first priority. We stop job assignment when all jobs are assigned. At the end, it can get the job assignment result and machine utilization. The case study shows that it is easy to use and to obtain load allocation and job assignment results rapidly through this model, considering process capability and machine dedication. The proposed Capacity Requirement Planning model hence is good for decision-making.