## 1 Introduction  
Printed circuit boards (PCB) are the key carriers of electrical connections between electronic components, and their quality directly affects the performance and life of electronic products [1]. However, their manufacturing process is highly complex, which can easily result in defects on its surface, such as missing holes, mouse bites, open circuits, short circuits, burrs, miscellaneous copper, etc. [2]. Because of this, PCB defect detection has become an indispensable part of PCB industrial production [3]. Traditional PCB defect detection relies on manual visual inspection, which depends entirely on operator skill and personal judgment [4]. It has several drawbacks: low efficiency, human bias, and failure to meet industrial quality control requirements. With the increasing recognition of these limitations, machine vision has gained growing popularity in the semiconductor manufacturing industry. The industry now widely employs automated optical inspection (AOI) systems based on machine vision [5]. However, AOI systems face inherent challenges, such as high costs, suboptimal performance in complex environments, and difficulties detecting tiny defects [6]. In contrast, deep learning-based PCB defect detection offers a superior solution, characterized by lower costs, faster processing speed, and higher accuracy [7]. These advantages have established deep learning as the leading technology for PCB inspection in the current landscape. Current deep learning-based object detection algorithms can be broadly categorized into two paradigms [8]. The first category comprises two-stage detection architectures, exemplified by the R-CNN [9] series, including Fast R-CNN [10] and Faster R-CNN [11]. These approaches employ a region proposal network to initially generate numerous candidate bounding boxes potentially containing target objects, followed by subsequent classification and regression operations performed on these proposals [12]. This hierarchical detection framework demonstrates particular suitability for PCB defect detection tasks, where precise localization and accurate classification of subtle defects are paramount. For PCB defect detection, [PERSON] et al. [13] proposed an advanced PCB defect detection algorithm using MAFF-Net, an enhanced Faster R-CNN architecture with ResNeXt101 as the feature extraction backbone. Their approach integrates a normalization-based attention mechanism within the residual structure and an AFF module with a coordinate attention mechanism, collectively achieving visible improvements in detection accuracy. [PERSON] et al. [14] developed a deep learning system based on the Faster R-CNN framework with ResNet101 as the backbone, incorporating a feature pyramid network (FPN) and replacing the ROI-Pooling algorithm with an ROI-Align approach utilizing bilinear interpolation. This modification mitigates feature map distortion caused by quantization during the interception period, yielding a high-performance detection network. [PERSON] et al. [15] optimized the Faster R-CNN architecture by using ResNet50 with an FPN, GARPN, and ShuffleNetV2\({}^{\prime}\)s residual units. The experimental results demonstrate that the system is more suitable for practical production applications compared to alternative methods. The second paradigm consists of single-stage detection architectures, with YOLO (You Only Look Once) as the archetype. These end-to-end detectors exhibit superior computational efficiency and broader applicability than two-stage models, though with a slight trade-off in detection accuracy. Recent advancements in YOLO-based frameworks have significantly enhanced PCB defect detection through innovative modifications. [PERSON] et al. [16] designed SF-YOLO by miniaturizing the detection head of YOLOv8, obtaining 0.6% mAP50 and 1.6% mAP50-95 improvements alongside 17.2% parameter reductions. [PERSON] et al.