FDC Based on Neural Network With Harmonic Sensor to Prevent Error of Robot

Abstract

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.