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Automatic Identification of Surface Defects in Semiconductor Materials Based on Machine Learning

Huan Li

Huan Li

University of Electronic Science and Technology of ChinaChengdu, China
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Li, Huan
  
17 mar 2025
Automatic Identification of Surface Defects in Semiconductor Materials Based on Machine Learning's Cover Image
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Pubblicato online: 17 mar 2025
Ricevuto: 08 ott 2024
Accettato: 04 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0271
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© 2025 Huan Li, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Defect area test results
Defect area test results

Figure 2.

Defect feature extraction
Defect feature extraction

Figure 3.

Normal semiconductor material surface and 8 defect modes
Normal semiconductor material surface and 8 defect modes

Figure 4.

Error identification of Random class
Error identification of Random class

Performance comparison of models (%)

Models Defect category Racc Rrec F
Decision tree algorithm Center 72.26 34.39 46.85
Torus 59.27 43.23 49.91
Marginal local 63.72 79.83 70.91
Edge ring 93.49 87.61 90.59
Local 57.33 48.73 52.66
Nearly full 93.35 93.27 93.49
Random 90.52 92.84 91.77
Scratches 85.30 92.28 88.58
SVM Center 84.75 77.15 80.85
Torus 47.36 80.85 59.62
Marginal local 87.55 81.54 84.34
Edge ring 94.30 86.75 90.26
Local 81.64 68.71 74.54
Nearly full 74.92 74.96 75.09
Random 89.92 99.35 94.4
Scratches 88.00 73.01 80.09
Random forest Center 76.4 85.46 80.73
Torus 95.26 64.63 77.11
Marginal local 79.76 91.32 85.00
Edge ring 96.13 81.47 88.06
Local 82.01 66.92 73.59
Nearly full 91.69 72.04 95.57
Random 95.1 87.41 97.31
Scratches 86.25 65.54 74.49
Ours Center 97.53 97.51 97.41
Torus 99.93 94.52 97.32
Marginal local 95.57 96.55 95.90
Edge ring 98.68 95.26 97.01
Local 95.86 96.23 96.02
Nearly full 94.15 94.17 94.08
Random 98.87 99.62 99.30
Scratches 89.64 87.24 88.40

Confusion matrix od model defect recognition rate (%)

Forecast reality Center Torus Marginal local Edge ring Local Nearly full Random Scratches
Center 97.69 0.00 0.00 0.57 0.00 0.68 1.06 0.00
Torus 1.48 95.63 0.64 0.00 0.32 0.00 0.29 1.64
Marginal local 0.00 2.81 96.94 0.00 0.03 0.09 0.11 0.02
Edge ring 1.06 0.81 0.98 95.79 0.00 0.37 0.83 0.16
Local 0.09 0.13 0.42 0.00 96.82 1.38 1.04 0.12
Nearly full 0.46 0.74 0.98 0.33 1.47 95.07 0.43 0.52
Random 3.92 0.00 4.08 0.00 4.73 3.53 83.74 0.00
Scratches 1.32 0.00 0.87 0.00 1.43 0.00 1.77 94.59

Comparison of five-fold cross validation of various algorithms

Models Racc
Decision tree algorithm 74.36
SVM 82.47
Random forest 85.39
Ours 96.82
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