Time series fault prediction in semiconductor equipment using recurrent neural network

Authors:
Javeria Muhammad Nawaz;Muhammad Zeeshan Arshad;Sang Jeen Hong
Affiliations:
Department of Electronic Engineering, Myongji University, Korea;Department of Electronic Engineering, Myongji University, Korea;Department of Electronic Engineering, Myongji University, Korea
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

Citing 4
Cited 0

Manufacturing ICs the neural way

IEEE Spectrum
Neural Networks in a Softcomputing Framework

Neural Networks in a Softcomputing Framework
Design of adaptive filter using Jordan/Elman neural network in a typical EMG signal noise removal

Advances in Artificial Neural Systems
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a model of Elman recurrent neural network (ERNN) for time series fault prediction in semiconductor etch equipment. ERNN maintains a copy of previous state of the input in its context units, as well as the current state of the input. Derivative dynamic time warping (DDTW) method is also discussed for the synchronization of time series data set acquired from plasma etcher. For each parameter of the data, the best ERNN structure was selected and trained using Levenberg Marquardt to generate one-step-ahead prediction for 10 experimental runs. The faulty experimental runs were successfully distinguished from healthy experimental runs with one missed alarm out of ten experimental runs.