In semiconductor manufacturing, data-driven methodologies have enabled the resolution of various issues, particularly yield management and enhancement. Yield, one of the crucial key performance indicators in semiconductor manufacturing, is mostly affected by production resources, i.e., equipment involved in the process. There is a lot of research on finding the correlation between yield and the status of resources. However, in general, multiple resources are engaged in production processes, which may cause multicollinearity among resources. Therefore, it is important to discover resource paths that are positively or negatively associated with yield. This article proposes a systematic methodology for discovering a resource-oriented transition system model in a semiconductor manufacturing process to identify resource paths resulting in high and low yield. The proposed method is based on the model-based analysis (i.e., finite state machine mining) in process mining and statistical analyses. We conducted an empirical study with real-life data from one of the leading semiconductor manufacturing companies to validate the proposed approach.

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发现面向资源的过渡系统以提高半导体制造的良率

在半导体制造中，数据驱动的方法论已解决各种问题，尤其是良率管理和增强。产量是半导体制造中关键的关键性能指标之一，主要受生产资源（即过程中涉及的设备）的影响。关于发现产量与资源状况之间的相关性的研究很多。但是，通常，在生产过程中会使用多种资源，这可能会导致资源之间的多重共线性。因此，重要的是发现与产量成正相关或负相关的资源路径。本文提出了一种系统方法，用于在半导体制造过程中发现面向资源的过渡系统模型，以识别导致高产量和低产量的资源路径。该方法基于过程挖掘和统计分析中基于模型的分析（即有限状态机挖掘）。我们对来自领先半导体制造公司之一的真实数据进行了实证研究，以验证所提出的方法。