## 1 Introduction  
The lithographic tool is important equipment for very large-scale IC manufacturing. Its function is to transfer the mask pattern into the photoresist on wafer. The process of lithography determines the integration of IC, which pushes forward the Moore's law. Nowadays, the designed feature size of IC is below 10 nm, and the number of transistors of an IC is as high as tens of billions [1, 2]. With the demand for high integration and better performance, the physical design of IC continues to shrink, and the lithographic printability has become one of the critical issues in IC design and manufacturing [3, 4]. Affected by the layout design and lithography process, the lithography results of some patterns in the layout are quite different from the target patterns, resulting in short-circuit or open-circuit problems. This problem will cause lithography hotspots. In order to reduce lithography hotspots, hotspot detection and layout correction are carried out in turn in the layout design stage. The performance of hotspot detection affects the turn-around time and the yield of IC manufacturing. Hotspot detection is one of the important techniques for IC design and manufacturing [3, 4, 5, 6]. A lot of hotspot-detection research has been carried out. The available hotspot-detection methods include the lithography simulation-based detection method [7, 8], the pattern matching -based detection method [5; 9; 10], and the machine-learning-based detection method [11; 12]. The lithography simulation-based hotspot-detection method predicts the lithography result on the wafer by physically simulating the lithography process, so as to find the corresponding hotspot areas on the layout. This method has high detection accuracy, but it is extremely time-consuming [13]. The pattern matching-based detection method detects hotspots by evaluating the degree of similarity between the layout patterns and the registered hotspot patterns. This method is fast, but it is invalid for unknown hotspot pattern [12; 13; 14; 15; 16]. The machine-learning-based detection method trains a model based on layout data, and the trained model is used to detect the hotspot patterns on the layout. This method is valid for unknown hotspot patterns with good detection performance. The machine-learning-based detection method has the advantage of high detection speed and has been widely studied [11; 12; 13; 14; 16]. Feature extraction and model design are important parts of the machine-learning-based hotspot-detection method. In order to obtain better performance, feature extraction has evolved from the artificially designed feature-extraction method [14; 17; 18] to using the convolutional neural network (CNN) [16; 19]. Model design has also been developed from shallow networks to deep networks [16; 20; 21]. In general, the deep learning networks have many layers, which require more training parameters and have a high cost of model training. Transfer learning can use a model trained with other datasets as a pre-trained model and fine-tune the pre-trained model with the target dataset to obtain a suitable model for the specified target design. In recent years, transfer learning has developed rapidly and has been widely used [22; 23; 24]. A transfer-learning-based hotspot-detection method has begun to emerge [25; 26]. Accuracy, recall, precision, and F1 score are commonly used as evaluation indicators for machine learning [27]. For hotspot detection, recall is related to the hotspot detection rate, precision is related to the false alarm, and the F1 score indicates the comprehensive performance of the model in terms of recall and precision. A good hotspot-detection model should perform well in F1 score, which means it has a high hotspot-detection rate and low false-alarm rate. Although the available machine-learning-based hotspot-detection methods perform well in recall, they still have insufficient precision and F1 score [16; 18; 19; 25; 26].