具垂直鎖定機台特性下之產能需求規劃之設計-以晶圓廠黃光區為例

Abstract

因應半導體製程技術的提昇，要求微影製程之線寬窄化的條件日益嚴謹。為能掌握微影製程線寬的規格要求，因此產品會被要求在具有該製程規格能力的機台加工，並針對不同製程層別會有綁機的限制。本文探究具綁機特性下之產能分配，使具不同製程規格能力之機台產能平衡，因而提出不同製程規格情境下的產能負荷分配模式，期望在考量產能負荷平衡下從事工單指派至機台之工作。 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 [1] 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 [3] 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。 本文兼顧綁機與不綁機的產能需求與工單指派問題，設計完整之產能規劃與決策流程。實驗結果顯示，透過電腦軟體之計算，能很快地產生相關產能負荷及工單指派資訊。此規劃流程可提供管理者容易快速獲得正確資訊以輔助決策。

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” [1] 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 [3] 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.