Scholarlink

<p>Positioning-shift failure is a major defect in wafer-handling robot arms that adversely affects the yield of semiconductor fabrication. This failure is often associated with wear on the belt teeth, which may lead to undesired slippage or sudden movements between the belt and gear. Coupled with potential self-correction during subsequent pick-up tasks, its intermittent nature poses challenges for testing and validation by robot manufacturers or in the laboratory. In this study, a real-time prognostic framework was developed for wafer-handling robot arms by integrating CCD-based eccentricity monitoring, temporal feature extraction, stacked LSTM prediction, and edge-enabled online deployment. The framework predicts the maximum eccentric quantity in the subsequent 1-min operating interval and supports online warning generation for operation-oriented failure prevention. The prediction results were in close agreement with the measured values, and the developed model maintained high predictive accuracy over extended operating periods. The platform also successfully updated monitoring information after each prediction cycle and automatically issued warning messages when the predicted eccentricity reached or exceeded the predefined warning threshold.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Real-time prognostics of the positioning-shift failure for wafer-handling robot arms using stacked LSTM deep-learning model and IoT edge computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

查看全文
  • Kuan-Jung Chung,
  • Jian-Hua Huang

摘要

Positioning-shift failure is a major defect in wafer-handling robot arms that adversely affects the yield of semiconductor fabrication. This failure is often associated with wear on the belt teeth, which may lead to undesired slippage or sudden movements between the belt and gear. Coupled with potential self-correction during subsequent pick-up tasks, its intermittent nature poses challenges for testing and validation by robot manufacturers or in the laboratory. In this study, a real-time prognostic framework was developed for wafer-handling robot arms by integrating CCD-based eccentricity monitoring, temporal feature extraction, stacked LSTM prediction, and edge-enabled online deployment. The framework predicts the maximum eccentric quantity in the subsequent 1-min operating interval and supports online warning generation for operation-oriented failure prevention. The prediction results were in close agreement with the measured values, and the developed model maintained high predictive accuracy over extended operating periods. The platform also successfully updated monitoring information after each prediction cycle and automatically issued warning messages when the predicted eccentricity reached or exceeded the predefined warning threshold.