摘要翻译：
SEMATECH赞助了J-88-E项目，与德克萨斯仪器公司和NeuroDyne（等）合作重点研究了半导体制造过程中所用的LAM9600铝等离子体刻蚀反应器的故障检测与分类(FDC)。故障分类是通过实现一系列虚拟传感器模型来实现的，这些模型使用来自真实传感器（Lam站传感器、发射光谱和射频监测）的数据来预测配方设定点和晶片状态特征。通过比较预测的配方值和晶片状态值与期望值进行故障检测和分类。所采用的模型包括线性PLS、多项式PLS和神经网络PLS。基于传感器数据的配方设定点预测提供了交叉检查机器是否保持所需设定点的能力。使用这些相同的工艺传感器(Lam、OES、RFM)在线估计晶片状态特征，如线宽减小和剩余氧化物。在生产环境中对这些特性的晶片到晶片的测量（其中通常在完成了具有许多晶片的批处理运行之后，该信息可能只有稀少的可用，如果有的话）将向操作员提供重要的信息，即该过程正在或不在产品质量的可接受范围内生产晶片。通过在蚀刻更多晶圆之前为操作者提供调整工艺或机器的机会，提高了生产成品率，并相应地降低了单位成本。
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英文标题：
《Virtual Sensor Based Fault Detection and Classification on a Plasma Etch
  Reactor》
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作者：
D. A. Sofge
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of these characteristics in a production setting (where typically this information may be only sparsely available, if at all, after batch processing runs with numerous wafers have been completed) would provide important information to the operator that the process is or is not producing wafers within acceptable bounds of product quality. Production yield is increased, and correspondingly per unit cost is reduced, by providing the operator with the opportunity to adjust the process or machine before etching more wafers.
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PDF链接：
https://arxiv.org/pdf/0706.0465