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首页> 外文期刊>IEEE transactions on electronics packaging manufacturing >Run-to-run failure detection and diagnosis using neural networks and Dempster-Shafer theory: an application to excimer laser ablation
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Run-to-run failure detection and diagnosis using neural networks and Dempster-Shafer theory: an application to excimer laser ablation

机译：使用神经网络和Dempster-Shafer理论进行的运行间故障检测和诊断：准分子激光烧蚀的应用

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摘要

The formation of microvias in multilayer substrates is a critical factor in microelectronic packaging manufacturing. Such microstructures can be produced efficiently by excimer laser ablation. Thus, laser ablation systems are evolving to a level where the need to offset high capital equipment investment and lower equipment downtime are imminent. This paper presents a methodology for inline failure detection and diagnosis of the excimer laser ablation process. The methodology employs response data originating directly from the equipment and characterization of microvias formed by the ablation process. Neural network (NN) models are trained and validated based on this data to generate evidential belief for potential sources of deviations in the responses. Dempster-Shafer (D-S) theory is adopted for evidential reasoning. Successful failure detection is achieved in 100% of 19 possible failure scenarios. Moreover, successful failure diagnosis is also achieved with only a single false alarm occurring in the 19 failure scenarios.
机译：多层基板中微孔的形成是微电子封装制造中的关键因素。这种微结构可以通过准分子激光烧蚀有效地产生。因此，激光烧蚀系统的发展水平已迫在眉睫，以抵消高额设备投资和减少设备停机时间的需求。本文提出了一种用于准分子激光烧蚀过程的在线故障检测和诊断的方法。该方法采用直接源自设备的响应数据以及由消融过程形成的微通孔的表征。基于此数据对神经网络（NN）模型进行训练和验证，以生成对于响应偏差的潜在来源的证据。证据推理采用了Dempster-Shafer（D-S）理论。在19种可能的故障场景中，有100％实现了成功的故障检测。此外，在19个故障场景中仅发生一个错误警报，也可以实现成功的故障诊断。

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