Operational Methods in Semiconductor Manufacturing: Methodsfor Modeling Stochastic Processes in Semiconductor Manufacturing

半导体制造中的操作方法：半导体制造中随机过程的建模方法

• 批准号: 9713549
• 负责人: Lee Schruben
• 金额: $ 80万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9713549&HistoricalAwards=false
• 关键词: Operational; Methods; Semiconductor; Manufacturing; Methodsfor

This award provides funding to investigate how the random elements of semiconductor manufacturing impact overall factory productivity. Algorithms will be created and tested for simulating dependent, transient random processes such as product demand, tool reliability, operator availability, and step yield. Statistical procedures will be developed for determining the types and amount of data that are needed to accurately represent these processes in factory queueing models and simulations. A central part of the research program will be designing efficient experimental procedures for optimizing the performance and validity of factory models. Experimental and analytical techniques for using these models to support the optimal timing of resource investments such as tool purchases and personnel cross-training will also be developed. If successful, this research will provide engineers in the semiconductor industry with fundamental knowledge necessary for the creation of valid models and efficient analytical tools useful in designing semiconductor fabrication facilities. A key to improving the cycle times and throughput of these multi-billion dollar factories is the identification and control of the random factors of production. Conventional semiconductor factory models assume that independent and identically distributed random input processes produce steady-state output processes with known performance objectives, economic parameters, and constraints. The realities of semiconductor fabrication are quite different. In the semiconductor industry, capacity requirements, product demands, and competitive cycle times are always changing, as are the very products, processes, and personnel skills involved. Currently, factory-level models are overly optimistic because they ignore many of the transient dependencies in the random phenomena found in real semiconductor manufacturing. This consistent optimism has had serious economic consequences. Valid factory models based on this research will be critical to the continued economic health of this vital industry. Furthermore, the costs of data collection are enormous. This research will provide guidelines for estimating how much data is necessary and how it should be used in engineering design. The modeling methodologies developed in this research will also help engineers to determine optimal settings of critical factory operating parameters that can make the difference between financial success and disaster.

该奖项为研究半导体制造中的随机因素如何影响整体工厂生产率提供了资金。算法将被创建和测试，以模拟相关的、瞬时的随机过程，如产品需求、工具可靠性、操作人员可用性和步骤产量。将开发统计程序，以确定在工厂排队模型和模拟中准确表示这些过程所需的数据类型和数量。研究计划的核心部分将是设计有效的实验程序，以优化工厂模型的性能和有效性。还将开发使用这些模型的实验和分析技术，以支持诸如工具购买和人员交叉培训等资源投资的最佳时机。如果成功，这项研究将为半导体行业的工程师提供必要的基础知识，以创建有效的模型和有效的分析工具，这些工具对设计半导体制造设施很有用。改善这些价值数十亿美元的工厂的周期时间和吞吐量的关键是识别和控制随机生产因素。传统的半导体工厂模型假设独立和同分布的随机输入过程产生具有已知性能目标、经济参数和约束的稳态输出过程。半导体制造的现实是完全不同的。在半导体行业中，容量需求、产品需求和竞争周期总是在变化，所涉及的产品、流程和人员技能也是如此。目前，工厂级模型过于乐观，因为它们忽略了实际半导体制造中发现的随机现象中的许多瞬态依赖关系。这种一贯的乐观主义产生了严重的经济后果。基于这项研究的有效工厂模型将对这个重要行业的持续经济健康至关重要。此外，数据收集的成本是巨大的。这项研究将为估计需要多少数据以及如何在工程设计中使用数据提供指导。本研究中开发的建模方法还将帮助工程师确定关键工厂操作参数的最佳设置，这些参数可以决定财务成功与灾难之间的差异。