Enhancing Confidence and Interpretability of a CNN-Based Wafer Defect Classification Model Using Temperature Scaling and LIME

Jieun Lee, Yeonwoo Ju, Junho Lim, Sungmin Hong, Soo-Whang Baek, Jonghwan Lee

Open source

DOI
10.3390/mi16091057
Published
09
Container
Micromachines
Publisher
Not recorded
Open access
yes

Credibility signals

uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.3390/mi16091057,
  title = {Enhancing Confidence and Interpretability of a CNN-Based Wafer Defect Classification Model Using Temperature Scaling and LIME},
  author = {Jieun Lee and Yeonwoo Ju and Junho Lim and Sungmin Hong and Soo-Whang Baek and Jonghwan Lee},
  year = {2025},
  journal = {Micromachines},
  doi = {10.3390/mi16091057},
  url = {https://doi.org/10.3390/mi16091057}
}

RIS

TY  - JOUR
TI  - Enhancing Confidence and Interpretability of a CNN-Based Wafer Defect Classification Model Using Temperature Scaling and LIME
AU  - Jieun Lee
AU  - Yeonwoo Ju
AU  - Junho Lim
AU  - Sungmin Hong
AU  - Soo-Whang Baek
AU  - Jonghwan Lee
PY  - 2025
JO  - Micromachines
DO  - 10.3390/mi16091057
UR  - https://doi.org/10.3390/mi16091057
ER  -

APA

Lee, J., Ju, Y., Lim, J., Hong, S., Baek, S., & Lee, J. (2025). Enhancing Confidence and Interpretability of a CNN-Based Wafer Defect Classification Model Using Temperature Scaling and LIME. Micromachines. https://doi.org/10.3390/mi16091057

Source records