## 4 Conclusions  
In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep CNN has been proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. Comparison with Samsung's hotspot-detection method based on deep CNNs and the hotspot-detection methods based on transfer learning in the past two years were carried out. The results show that the proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. Compared with Samsung's deep CNN-based hotspot-detection method, the average precision and average F1 score are improved by 298% and 159%, respectively. The improvement of precision and F1 score indicates the proposed hotspot-detection method has a low false-alarm rate. A low false-alarm rate will decrease the post-processing steps and decrease the turn-around time of IC manufacturing. In order to test the effect of updating the weights of the convolutional layers on the results, partial convolutional layers were released for model training. Compared with freezing all convolutional layers, the results show that updating the weights of partial convolutional layers has little effect on the results of this method. The proposed method has the characteristics of low training cost, simple model architecture, and good performance. The proposed method is suitable for lithography hotspot detection. Conceptualization, L.L. ; methodology, L.L. ; software, L.L. ; validation, L.L. ; formal analysis, L.L. ; investigation, L.L. ; resources, L.L. and S.L. ; data curation, L.L. ; writing--original draft preparation, L.L. ; writing--review and editing, L.L., S.L., X.W., Y.C. and W.S. ; visualization, L.L. ; supervision and project administration, S.L. and X.W. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Benchmarks** & **Methods** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline \multirow{4}{*}{Benchmark 1} & Ref. [16] & - & 0.951 & 0.358 & 0.520 \\  & Ref. [26] & - & 0.995 & 0.324 & 0.489 \\  & Case 1 & 0.965 & 0.947 & 0.968 & 0.957 \\  & Case 2 & 0.965 & 0.942 & 0.973 & 0.957 \\ \hline \multirow{4}{*}{Benchmark 2} & Ref. [16] & - & 0.988 & 0.216 & 0.354 \\  & Ref. [26] & - & 0.986 & 0.702 & 0.820 \\  & Case 1 & 0.980 & 0.988 & 0.850 & 0.914 \\  & Case 2 & 0.977 & 0.980 & 0.837 & 0.903 \\ \hline \multirow{4}{*}{Benchmark 3} & Ref. [16] & - & 0.975 & 0.199 & 0.331 \\  & Ref. [26] & - & 0.982 & 0.443 & 0.640 \\  & Case 1 & 0.984 & 0.988 & 0.967 & 0.977 \\  & Case 2 & 0.985 & 0.986 & 0.969 & 0.978 \\ \hline \multirow{4}{*}{Benchmark 4} & Ref. [16] & - & 0.938 & 0.157 & 0.269 \\  & Ref. [26] & - & 0.972 & 0.355 & 0.520 \\  & Case 1 & 0.992 & 0.937 & 0.913 & 0.925 \\  & Case 2 & 0.991 & 0.927 & 0.903 & 0.915 \\ \hline \multirow{4}{*}{Benchmark 5} & Ref. [16] & - & 0.927 & 0.181 & 0.303 \\  & Ref. [26] & - & 0.980 & 0.549 & 0.635 \\  & Case 1 & 0.993 & 1.000 & 0.737 & 0.848 \\  & Case 2 & 0.992 & 1.000 & 0.712 & 0.832 \\ \hline \multirow{4}{*}{Average} & Ref. [16] & - & 0.955 & 0.222 & 0.355 \\  & Ref. [26] & - & 0.983 & 0.475 & 0.635 \\ \cline{1-1}  & Ref. [25] & - & 0.980 & 0.300 & 0.458 \\ \cline{1-1}  & Case 1 & 0.983 & 0.972 & 0.887 & 0.924 \\ \cline{1-1}  & Case 2 & 0.982 & 0.967 & 0.879 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of case 1, case 2, and the references. **Funding:** This research was funded by National Science and Technology Major Project of China (grant number: 2017 ZX02101004-002, 2017 ZX02101004). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.