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Convolutional neural network model based on terahertz imaging for integrated circuit defect detections
- Source :
- Optics express. 28(4)
- Publication Year :
- 2020
-
Abstract
- Detection of integrated circuit (IC) defects is vital in IC manufacturing. Traditional defect detection methods have relied on scanning electron microscopy and X-ray imaging techniques that are time consuming and destructive. Hence, in this paper we considered terahertz imaging as a label-free and nondestructive alternative. This study aimed to use a convolutional neural network model (CNN) to improve the performance of the terahertz imaging IC detection system. First, we constructed a terahertz imaging IC dataset and analyzed it. Subsequently, a new CNN structure was proposed based on the VGG16 network. Finally, it was optimized based on its structure and dropout rate. The method proposed above can improve IC defects detection accuracy of THz imaging. Most significantly, this work will promote the application of terahertz imaging in practice and provide a foundation to further research in relevant fields.
- Subjects :
- business.industry
Computer science
Scanning electron microscope
Terahertz radiation
02 engineering and technology
Integrated circuit
021001 nanoscience & nanotechnology
01 natural sciences
Convolutional neural network
Atomic and Molecular Physics, and Optics
law.invention
010309 optics
Optics
law
0103 physical sciences
Electronic engineering
0210 nano-technology
business
Dropout (neural networks)
Subjects
Details
- ISSN :
- 10944087
- Volume :
- 28
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Optics express
- Accession number :
- edsair.doi.dedup.....20e7f58c41ae531274e455ec67f47b5a