A novel ontology-based knowledge engineering approach for yield symptom identification in semiconductor manufacturing

Fang-Hsiang Su, Shi-Chung Chang, Chih-Min Fan, Ya-Jung Tsai, J. Jheng, Ching-Pin Kao, Chun-Yao Lu
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引用次数: 1

Abstract

Effective management of knowledge-intensive yield analysis plays a significant role in fast yield ramping for semiconductor manufacturing. Although data analysis platforms with many analysis function tools are available to the industry, there is lack of systematic representation of engineering knowledge for effective extraction and sharing; engineers' identification of situations and analysis purposes and flows are largely in engineers' minds or in disparate forms. In this paper, over the problem domain of fault symptom identification for semiconductor yield analysis, a novel ontology-based modeling framework is first designed for knowledge representations across data, function flow and purpose layers. The ontology model facilitates the knowledge descriptions of an engineer's analysis purpose plan, the application sequences of analysis tools as well as the mapping between a purpose and tool selections. To substantiate the ontology framework with modeling contents, three methods are designed: a Markov chain-based algorithm to extract from engineers' analysis log data their procedures and preferences of tool applications, a tree construction algorithm for engineers' analysis purpose planning, and a graphic symptom capturer for auto-capturing of perceived fault symptoms by engineers. Such designs have been integrated into an engineering data analysis platform that enables engineers' effective extraction, sharing, and reuse of knowledge in situation identification, purpose planning and tool applications.
基于本体的半导体制造良率症状识别知识工程方法
知识密集型良率分析的有效管理对半导体制造的快速良率提升起着重要的作用。虽然业界已经有了具有多种分析功能工具的数据分析平台,但缺乏对工程知识的系统表示,无法进行有效的提取和共享;工程师对情况、分析目的和流程的识别主要是在工程师的头脑中或以不同的形式存在。本文针对半导体成品率分析故障症状识别的问题域,设计了一种新的基于本体的建模框架,用于跨数据层、功能流层和目的层的知识表示。本体模型有利于工程师分析目的计划的知识描述、分析工具的应用顺序以及目的与工具选择的映射。为了使本体框架具有建模内容,设计了三种方法:一种基于马尔可夫链的算法,用于从工程师的分析日志数据中提取工程师的分析过程和工具应用偏好;一种基于树的构建算法,用于工程师的分析目的规划;一种图形症状捕获器,用于工程师自动捕获感知到的故障症状。这些设计已经集成到一个工程数据分析平台中,使工程师能够在情况识别、目的规划和工具应用中有效地提取、共享和重用知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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