Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system

1997 ◽  
Vol 20 (1) ◽  
pp. 39-47 ◽  
Author(s):  
B. Kim ◽  
G.S. May
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Hybrid Neural Network ◽  
Manufacturing Equipment ◽  
Time Diagnosis

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

Real-time Diagnosis System Development of Common Rail Diesel Based on Expert System

2005 ◽  
Author(s):  
Guomin Song ◽  
Fuyuan Yang ◽  
Minggao Ouyang ◽  
Jun Li ◽  
Linfeng Hu
Keyword(s):  
Expert System ◽  
Real Time ◽  
System Development ◽  
Common Rail ◽  
Diagnosis System ◽  
Time Diagnosis

Combat Unit Selection Based on Hybrid Neural Network in Real-Time Strategy Games

2021 ◽  
pp. 344-352
Author(s):  
Hongcun Guo ◽  
Zhaoxiang Zang ◽  
Zhen Zhang ◽  
Pei Tian
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Unit Selection ◽  
Hybrid Neural Network ◽  
Strategy Games ◽  
Combat Unit

scholarly journals The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haijing Tang ◽  
Taoyi Wang ◽  
Mengke Li ◽  
Xu Yang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fetal Heart ◽  
Current Medical Research ◽  
Classification Model ◽  
Classification Method ◽  
Portable Devices ◽  
Feature Based ◽  
Feature Acquisition ◽  
Time Diagnosis

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

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