A SelectiveNet-Based Method for Defect Classification in Semiconductor Manufacturing
- Resource Type
- Conference
- Authors
- Jin, Qian; Qiao, Yibo; Chen, Yining; Zhuo, Cheng; Sun, Qi
- Source
- 2024 Conference of Science and Technology for Integrated Circuits (CSTIC) Science and Technology for Integrated Circuits (CSTIC), 2024 Conference of. :1-3 Mar, 2024
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Photonics and Electrooptics
Semiconductor device modeling
Deep learning
Scanning electron microscopy
Focusing
Production
Semiconductor device manufacture
Predictive models
Automatic Defect Classification(ADC)
Selective Learning
Scanning Electron Microscope(SEM)
Semi-conductor Manufacturing
Deep Learning
- Language
In semiconductor manufacturing, accurately classifying defects in scanning electron microscope (SEM) images is crucial for optimizing the production process. This paper introduces a novel defect classification frame-work based on SelectiveNet, which can reject predictions for defects with a high risk of misclassification through selective learning, effectively balancing the trade-offs between prediction coverage and accuracy for highly diverse and complex real SEM images. The efficacy of our proposed approach is demonstrated in the experiments with 95.14% classification accuracy with no reject and 98.37% at a selective learning coverage of 91.17% (rejection of 8.83%).