Application of Artificial Intelligence to Improve Chip Defect Detection Using Semiconductor Equipment †
Abstract
1. Introduction
2. Literature Review
2.1. Traditional AOI Technology
2.2. AI Technology
2.2.1. Machine Learning in Defect Detection
2.2.2. AI and AOI
3. Research Methodology
- H1: By utilizing advanced algorithms such as CNN in deep learning, the accuracy of AOI systems is improved in identifying chip defects, thereby reducing the overkill rate.
- H2: By precisely setting detection parameters and combining the learning capabilities of AI models, the system adapts to different environmental changes, reducing misjudgments caused by environmental factors.
- H3: Introducing AI technology into AOI systems shortens the inspection time and reduces the need for manual re-inspection, thereby lowering costs and enhancing manufacturing efficiency.
3.1. CNN in AOI System
3.2. Reducing Defective Rate
4. Results
4.1. First Test Results
- FP = False Positive rate × Total sample size = 13.5% × 200 = 27 units.
- 86.5%
- 52.63%
- Recall %
- F1Score 0.6897%
4.2. Second Detection Result
- %
- %
- Recall %
- F1Score 0.8333%
4.3. Comparison of Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number of Tests | Illustrate |
---|---|
Introducing AI software programs, first detection | Using a CNN model, a large amount of annotated wafer image data is used to train the CNN model so that it can learn the characteristics of various defects and normal conditions. Test results: The overkill rate is decreased from 200 pieces to 27 pieces. Note: The CNN model improves the detection accuracy and successfully identifies 173 actual qualified wafers, avoiding unnecessary elimination. |
Through AI response system program, second test | In the first inspection, the 27 NG wafers that are still misjudged after using the AI response mechanism are manually re-inspected, and the misjudged samples are fed back to the CNN model for retraining. Test results: The overkill rate is further reduced to 13 tablets. Explanation: Through feedback learning, the model corrects previous incorrect judgments and further improves detection accuracy. |
A-1 | B-1 | A-2 | B-2 |
Figure A-1 is the first pollution detection image, and B-1 is the second training image. | Figure A-2 is the first image of detecting surface pollution particles, and B-2 is the second training image. | ||
A-3 | B-3 | A-4 | B-4 |
Figure A-3 is the first scratch detection image, and B-3 is the second training image. | Figure A-4 is the first scratch detection image, and B-4 is the second training image. | ||
A-5 | B-5 | A-6 | B-6 |
Figure A-5 is the first detection indentation image, and B-5 is the second training image. | Figure A-6 is the first discoloration detection image, and B-6 is the second training image. |
T (Defective Product) | F (Good Product) | |
---|---|---|
P (detected as defective product) | True positive (TP) | False positive (FP) (misjudgment) |
N (tested as good product) | True negative (TN) | False negative (FN) (misjudgment) |
T | F | |
P | 30 pieces (assuming that all defective products are detected and no judgments are missed) | 27 (calculated) |
N | 170 − 27 = 143 pieces | 0 (assuming no missed judgments) |
T | F | |
---|---|---|
P | 30 pieces (assuming all defective products are detected and no judgments are missed) | 12 pieces |
N | 170 − 12 = 158 pieces | 0 (assuming no missed judgments) |
Detection Value | First Test | Second Test | Aaccuracy |
---|---|---|---|
False positive rate | 13.5% | 6% | 13.5% − 6% = 7.5% |
86.5% | 94% | 94% − 86.5% = 7.5 | |
Precision | 52.63% | 71.43% | 71.43% − 52.63% = 18.8% |
Recall | 100% | 100% | - |
F1 score | About (0.6897) | About (0.8333) | 0.8333–0.6897 = 0.1436 |
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Fu, C.-J.; Chen, H.-L.; Tseng, H.-Y. Application of Artificial Intelligence to Improve Chip Defect Detection Using Semiconductor Equipment. Eng. Proc. 2025, 98, 26. https://doi.org/10.3390/engproc2025098026
Fu C-J, Chen H-L, Tseng H-Y. Application of Artificial Intelligence to Improve Chip Defect Detection Using Semiconductor Equipment. Engineering Proceedings. 2025; 98(1):26. https://doi.org/10.3390/engproc2025098026
Chicago/Turabian StyleFu, Chung-Jen, Hsuan-Lin Chen, and Huo-Yen Tseng. 2025. "Application of Artificial Intelligence to Improve Chip Defect Detection Using Semiconductor Equipment" Engineering Proceedings 98, no. 1: 26. https://doi.org/10.3390/engproc2025098026
APA StyleFu, C.-J., Chen, H.-L., & Tseng, H.-Y. (2025). Application of Artificial Intelligence to Improve Chip Defect Detection Using Semiconductor Equipment. Engineering Proceedings, 98(1), 26. https://doi.org/10.3390/engproc2025098026