基于神经网络谐波传感器的FDC机器人误差预防

Kenta Kamizono, Kazutaka Ikeda, Hiroaki Kitajima, S. Yasuda, Tomoya Tanaka
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引用次数: 1

摘要

为了进一步提高制造设备的生产率,对所有制造设备的状态进行监测是必不可少的,而不仅仅是对加工室的状态进行监测。本文提出了一种基于鲁棒机器学习的谐波传感器退化诊断技术。离子注入机、LP-CVD和镀膜机/显影剂中的晶圆传递机器人的例子表明,可以从降解输出的水平检测到机器部件的磨损退化,并且可以防止晶圆传递机器人由于基于降解水平的增加而产生的维护错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FDC Based on Neural Network with Harmonic Sensor to Prevent Error of Robot
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 degradation diagnosis technology with harmonic sensor. The example of wafer transfer robots in the ion implanter and the LP-CVD and the coater/developer show that wear degradation of machine components can be detected from the level of degradation output and it is possible to prevent errors of the wafer transfer robots because of maintenance based on increases in the level of degradation.
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