Abstract
The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.
Highlights
In a semiconductor production factory, a module of equipment that has malfunctioned or shut down must generally be repaired or replaced with a new module within the shop floor system.This activity results in equipment downtime and considerable maintenance costs, which, in turn, yield loss of production profit
From Day 1, the CV indicating the health status was not equal to 1 when the spread parameter σ was tuned by the genetic algorithm (GA)
Because the general regression neural network (GRNN) uses probabilistic-based network regression, certain standard deviations were likely to be within the prediction range
Summary
In a semiconductor production factory, a module of equipment that has malfunctioned or shut down must generally be repaired or replaced with a new module within the shop floor system. This activity results in equipment downtime and considerable maintenance costs, which, in turn, yield loss of production profit. A wafer-handling robot arm (WHRA) is used to transfer wafers between the cassette and the chamber. Machine manufacturers have provided a solution, i.e., position compensation of robot arms, in the latest generation of machines, manufacturing engineers have attempted to upgrade old machines by implementing new controller boards. An effective method for predicting maintenance time and a reliable kit for adding a positioning inspection module to existing equipment are required [1]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
