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Developing a Product Quality Fault Detection Scheme

机译：开发产品质量故障检测方案

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In current semiconductor and TFT-LCD factories, periodic sampling is commonly adopted to monitor the stability of manufacturing processes and the quality of products (or workpieces). As for those non-sampled workpieces, their quality is usually monitored by such as a fault-detection-and-classification (FDC) server. However, this method may fail to detect defected products. For example, a workpiece with all the individual manufacturing process parameters being in-spec may still result in out-of-spec product quality. Under this circumstance, unless this certain defected workpiece is selected for sampling by chance, it cannot be detected by simply monitoring the manufacturing process parameters collected from the production equipment. To solve the abovementioned problem, this research proposes a product quality fault detection scheme (FDS), which utilizes the classification and regression tree to implement a model for identifying the relationship between process parameters and out-of-spec products. Through this model, each set of normal manufacturing process parameters can be real-time and on-line examined to detect failure or defected products.
机译：在目前的半导体和TFT-LCD工厂中，通常采用周期性采样来监测制造工艺的稳定性和产品质量（或工件）。至于那些未采样的工件，通常通过诸如故障检测和分类（FDC）服务器等质量。但是，这种方法可能无法检测缺陷的产品。例如，具有符合规范的所有单独制造工艺参数的工件可能仍会导致规范超出产品质量。在这种情况下，除非选择一定的缺陷的工件，否则通过机会选择采样，否则无法通过简单地监测从生产设备收集的制造过程参数来检测。为了解决上述问题，本研究提出了产品质量故障检测方案（FDS），其利用所述的分类和回归树来实现模型用于识别过程参数和外的规格产品之间的关系。通过该模型，每组正常制造过程参数可以是实时和在线检查以检测失败或缺陷的产品。

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