Wafer map failure pattern recognition based on deep convolutional neural network-Reference-Cited by-同舟云学术

Wafer map failure pattern recognition based on deep convolutional neural network

Published:2022-12 Issue: Volume:209 Page:118254
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Chen Shouhong,Zhang Yuxuan,Hou Xingna,Shang Yuling,Yang Ping^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference23 articles.

1. Machine-Learning-Based Identification of Defect Patterns in Semiconductor Wafer Maps: An Overview and Proposal;Adly;IEEE International Parallel & Distributed Processing Symposium Workshops,2014

2. Identifying Wafer-Level Systematic Failure Patterns via Unsupervised Learning;Alawieh;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2018

3. An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing;Chien;Journal of Intelligent Manufacturing,2014

4. Operational planning and control of semiconductor wafer production;Gupta;Production Planning & Control,2006

5. Using regression models for predicting the product quality in a tubing extrusion process;García;Journal of Intelligent Manufacturing,2019

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