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首页> 外文会议>IFIP WG 5.7 international conference on advances in production management systems >Construct an Intelligent Yield Alert and Diagnostic Analysis System via Data Analysis: Empirical Study of a Semiconductor Foundry
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Construct an Intelligent Yield Alert and Diagnostic Analysis System via Data Analysis: Empirical Study of a Semiconductor Foundry

机译：通过数据分析构建智能的产量预警和诊断分析系统：一家半导体代工厂的实证研究

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摘要

As semiconductor manufacturing technology advances, the process becomes longer and more complex. A critical issue is to determine how to avoid yield loss at an early stage or to diagnose the cause of yield loss soon, in order to save more money. Traditional statistical regression analysis and correlation analysis are unable to quickly and easily figure out the causes of process anomalies and potential problems. This study aims to construct an intelligent yield alert and diagnostic analysis framework combined within a big data analysis architecture. Through an intelligent detection and early warning mechanism, instant detection of yield anomalies and automatic diagnostic analysis based on good/bad wafer classification, we can effectively and rapidly find out the factors that may cause process variation to help quickly clarify the causes of abnormal product yield. The case study in this paper uses real-world data from a foundry in Taiwan. We hope to provide engineers and domain experts with a reference framework for building a yield analysis system to help improve the yield of semiconductor manufacturing and enhance the competitiveness of high-tech industries.
机译：随着半导体制造技术的进步，该过程变得更长且更复杂。一个关键问题是确定如何在早期阶段避免产量损失或尽快诊断产量损失的原因，以节省更多的钱。传统的统计回归分析和相关分析无法快速轻松地找出过程异常的原因和潜在的问题。这项研究旨在构建结合在大数据分析架构中的智能良率警报和诊断分析框架。通过智能的检测和预警机制，即时检测良率异常以及基于良/劣晶圆分类的自动诊断分析，我们可以有效，快速地找出可能导致工艺变化的因素，从而帮助快速弄清产品良率异常的原因。 。本文中的案例研究使用了台湾一家铸造厂的真实数据。我们希望为工程师和领域专家提供参考框架，以建立收益分析系统，以帮助提高半导体制造的收益并增强高科技产业的竞争力。

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