## 3 Results and Discussion  
In order to verify the validity of the proposed method, the VGG13 network trained with the ImageNet dataset was used for the pre-trained model, and ICCAD 2012 benchmark suite [30] is used for model training and model verification. ICCAD 2012 benchmark suite consists of five benchmarks, each of which contains training data and testing data. The composition of ICCAD 2012 benchmark suite is shown in Table 1. Based on the data-compression method in the preparation phase, the layout data of ICCAD 2012 benchmark suite are converted to 240 \(\times\) 240 pixels pattern data. According to the data-balance method, non-hotspot data are randomly sampled in the training data of ICCAD 2012 benchmark suite. The randomly sampled non-hotspot data and all hotspot data constitute a complete training dataset; the number of data in the training dataset is 5054. The model verification dataset is the combination of the testing data of ICCAD 2012 benchmark suite. Model training and model verification were performed on a server platform with Intel Xeon Gold 5118 CPU, 128 GB RAM, and Nvidia Tesla V100 GPU.  
The pre-trained VGG13 network was trained with the prepared training dataset. The training curves and validation curves are shown in Figure 8, after 10 epochs of training, the validation curves almost no longer change. After 10 epochs of training, the model is used for model verification to evaluate the performance. Samsung's hotspot-detection method based on deep CNN [16] ([PERSON]'s method) and the hotspot-detection methods based on transfer learning in the past two years [25; 26] ([PERSON]'s method and [PERSON]'s method) are used as references. The hotspot-detection method of Ref. [25] is based on different workflow, and it is based on Inception-v3, ResNet50, and VGG16 networks. Additionally, the hotspot-detection method of Ref. [26] is based on the GoogLeNet network and different workflow. The results of accuracy, recall, precision, and F1 score are compared with the results of references. Since the convolutional layers can be partially released, model training and model verification were performed in the following situations:  
(1) In the model-training phase, only the fully connected layers of the pre-trained VGG13 network were released. After 10 epochs of training, the model was evaluated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Benchmarks**} & \multicolumn{2}{c}{**Training Data**} & \multicolumn{2}{c}{**Testing Data**} \\ \cline{2-5}  & **Hotspots** & **Non-Hotspots** & **Hotspots** & **Non-Hotspots** \\ \hline Benchmark 1 (B1) & 99 & 340 & 224 & 319 \\ Benchmark 2 (B2) & 174 & 498 & 498 & 4146 \\ Benchmark 3 (B3) & 909 & 1808 & 1808 & 3541 \\ Benchmark 4 (B4) & 95 & 4452 & 177 & 3386 \\ Benchmark 5 (B5) & 26 & 2716 & 41 & 2111 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition of ICCAD 2012 benchmark suite.  
Figure 8: The loss and accuracy curves. (**a**) The loss and accuracy curves of training; (**b**) the loss and accuracy curves of validation.  
(2) In the model-training phase, the fully connected layers and partial convolutional layers of the pre-trained VGG13 network were released. After 10 epochs of training, the model was evaluated.