Automated Wafer Defect Classification using a Convolutional Neural Network Augmented with Distributed Computing
- Resource Type
- Conference
- Authors
- Lei, Hairong; Teh, Cho; Li, Hetong; Lee, Po-Hsuan; Fang, Wei
- Source
- 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 2020 31st Annual. :1-5 Aug, 2020
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Photonics and Electrooptics
Power, Energy and Industry Applications
Machine learning
Support vector machines
Training
Forestry
Convolutional neural networks
Computer architecture
Classification algorithms
Wafer defect classification
Convolutional Neural Network (CNN)
Deep Learning
Distributed Computing
- Language
- ISSN
- 2376-6697
This research compares the traditional machine learning algorithms and deep learning technology. We report our distributed computing convolutional neural network deep learning platform design and results in wafer defect classification. The result shows that the classification accuracy and purity performance is better than that of traditional machine learning models like Random Forest.