蒋小康¹, 张朋², 吕佑龙¹, 赵新明², 张洁¹()

1.东华大学 机械工程学院, 上海 216020
2.上海交通大学 机械与动力工程学院, 上海 200240

收稿日期:2018-07-02出版日期:2020-08-28发布日期:2020-08-18
通讯作者:张洁E-mail:mezhangjie@dhu.edu.cn
作者简介:蒋小康(1994-),男,浙江省衢州市人,硕士生,主要从事半导体制造系统调度研究
基金资助:国家自然科学基金资助项目(51435009);国家自然科学基金资助项目(U15371110)

Hybrid Ant Colony Algorithm for Batch Scheduling in Semiconductor Furnace Operation

JIANG Xiaokang¹, ZHANG Peng², Lü Youlong¹, ZHAO Xinming², ZHANG Jie¹()

1. School of Mechanical Engineering, Donghua University, Shanghai 216020, China
2. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2018-07-02Online:2020-08-28Published:2020-08-18
Contact:ZHANG Jie E-mail:mezhangjie@dhu.edu.cn

摘要/Abstract

摘要： 炉管区是半导体生产线的主要瓶颈之一,对整个生产线的性能影响较大.当前针对炉管区调度研究以规则调度为主,且考虑约束较为简单,忽略了存在前后道工序影响的多机台调度以及晶圆加工的重入特性.针对具有等待时间约束、不兼容工艺菜单和重入特性的炉管区β₁→β₂调度问题,构建了目标为最小化晶圆平均流动时间的β₁→β₂调度模型.将该调度问题分成组批、设备选择及批排序3个阶段,提出了一种基于混合蚁群优化算法的炉管区调度算法.针对组批阶段,设计了一种可变阈值控制策略.针对批排序阶段,设计了混合蚁群优化算法.进行了历史生产数据的不同规模54组算例实验,结果表明:混合蚁群算法的性能均优于几种常用启发式规则和遗传算法的性能.将所提出的混合蚁群算法应用于实际晶圆生产线,能够有效减少生产过程中晶圆的流动时间.
关键词: 半导体制造, 炉管, 批调度, 混合蚁群算法
Abstract: Furnace district is one of the main bottlenecks in semiconductor fabrication, which has a great influence on the entire production line. The current scheduling research in the furnace district mainly focuses on dispatching rules, and the constraints considered are relatively simple. The previous research ignores not only the multi-machine scheduling which contains front and rear procedures but also the re-entrant characteristic of the wafer fabrication. This paper focuses on the scheduling problem of β₁→β₂ type for minimizing the meaning flow time (MFT) in furnace district. The constraints consist of limited waiting time, incompatible families, and re-entrant flow. It builds a novel β₁→β₂ model about the scheduling problem, and decomposes the problem into three stages: batch forming, machine selecting, and batch sorting. An algorithm based on the hybrid ant colony optimization algorithm is proposed, which batches the jobs by using a variable threshold control strategy, and sorts these batches by a hybrid ant colony optimization algorithm. According to the results of 54 sets of different scales based on historical production data, it is concluded that the performance of the hybrid ant colony optimization (ACO) algorithm is better than several common heuristic rules and the genetic algorithm. The proposed hybrid-ACO algorithm is applied to the actual wafer production line, which can effectively reduce the water flow time in the production process.
Key words: semiconductor manufacture, furnace, batch scheduling, hybrid ant colony algorithm


PDF全文下载地址:

点我下载PDF