# Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
## 1 Introduction  
Semiconductor chips, which are used in industries and electronic products that we often talk about, are semiconductor integrated circuits. Semiconductor wafers can be manufactured by eight different processes [1], but these are largely divided into front-end and back-end processes. The front-end process is used to design a semiconductor chip and engrave in on the wafer; the back-end process is used to cut the chip engraved on the wafer and wrap it with an insulator to lay wires to receive power stably [2, 3]. In particular, in the front-end process, also called the wafer process, a single semiconductor chip is made by repeatedly forming and cutting various types of films on the wafer surface to create an electronic circuit [4]. Techniques such as Photolithography, which transfers semiconductor circuit patterns onto a wafer; Etching, which cuts off parts other than circuit patterns; Deposition, which forms an insulating thin film for separation and protection between metal, circuitry, and metal for transmitting electrical signals; and Metallization, which forms wiring, are included in the previous process [5, 6]. Because of these various processes, the types of defects that occur on the wafer are also diverse. After wafer fabrication, several tests are performed to identify defects and represent them as binary values on the wafer map. In this way, the classification results for the dies on the wafer map form a specific pattern and are visually displayed [7]. The various defect patterns on the wafer map are related to the manufacturing process. Therefore, accurately classifying the defect patterns on the wafer map allows defect sources from the manufacturing process to be identified. Such classification is also important because it provides engineers with clues for solving problems [8]. Because of recent advances in miniaturization technology and an increase in wafer size, the probability of generating two or more mixed defect patterns has increased [9, 10]. Detecting defect patterns is more complex, because mixed defects can have many combinations of causes, such as location, size, type, and number. Recently, because of the development of artificial intelligence, research based on deep learning is being widely conducted. [PERSON] and [PERSON] [11] proposed using convolutional neural networks to classifymixed defect patterns, and [PERSON] et al. [12] proposed deformable convolutional networks. [PERSON] et al. [13] proposed an infinite warped mixture model for clustering mixed defect patterns. [PERSON] et al. [14] proposed data augmentation and mask R-CNN for instance segmentation in mixed-type defect patterns. As a method for detecting defects, segmentation is used one step further from classification. Classification simply classifies the target image, while segmentation can infer data on a pixel-by-pixel basis. This can greatly aid in decision making by providing additional information to the user. In particular, many segmentation studies are being conducted for the purpose of finding diseases in the medical field. [PERSON] et al. [15] proposed a model applying an attention gate to U-Net for medical image segmentation. Through the attention gate, the model learns by automatically focusing on target structures of various shapes and sizes. [PERSON] et al. [16] proposed a novel U-Net architecture using aggregated residual blocks and a soft attention mechanism for segmentation of regions infected with COVID-19. [PERSON] et al. [17] proposed a cascaded atrous dual-attention U-Net for accurate tumor segmentation. They introduced a cascade structure to extend low-resolution quality prediction, and proposed a dual-attention module to improve the functional expression of tumor segmentation. In this study, the residual attention block and the integrated residual block and attention module were combined with U-Net to provide engineers with segmentation results focused on the defect area. We make specific contributions as follows:  
1. Applying an attention-guided U-Net for classification of wafer defects. 2.