In order to further improve the productivity of manufacturing equipment, it is indispensable to monitor the conditions of all the manufacturing equipment and not just the processing chambers. In this paper, we present a robust machine learning based deterioration diagnosis technology with harmonic sensor, which has frequency characteristics that have high sensitivity to deterioration and low sensitivity to torque. The robust deterioration diagnosis using a small amount of data is possible by limiting the input data to be trained and inferred to signals and motion patterns, and inferring with neural network models that do not require developments for manufacturing equipment. The example of wafer transfer robots in the ion implanter and the Low-Pressure Chemical Vapor Deposition (LP-CVD) and the coater/developer show that wear deterioration of machine components can be detected from the level of deterioration output and it is possible to prevent errors of the wafer transfer robots because of maintenance based on increases in the level of deterioration.

中文翻译：

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基于带谐波传感器的神经网络的FDC防止机器人错误

为了进一步提高制造设备的生产率，监控所有制造设备的状态是必不可少的，而不仅仅是处理室。在本文中，我们提出了一种基于谐波传感器的稳健机器学习劣化诊断技术，该技术具有对劣化具有高灵敏度和对扭矩低灵敏度的频率特性。通过将要训练和推断的输入数据限制为信号和运动模式，并使用不需要开发制造设备的神经网络模型进行推断，可以使用少量数据进行稳健的劣化诊断。