In the manufacturing of chips, the accurate and effective detection of internal bubble defects of chips is essential to maintain product reliability. In general, the inspection is performed manually by viewing X-ray images, which is time-consuming and less reliable. To solve the above problems, an improved bubble defect detection model YOLO-Xray based on the YOLOv5 algorithm for chip X-ray images is proposed. First, the chip X-ray images are preprocessed by image segmentation to construct the chip X-ray defect dataset, namely, CXray. Then, in the input stage, the K-means++ algorithm is used to re-cluster the CXray dataset to generate the anchors suitable for our dataset. In the backbone network, a micro-scale detection head is added to improve the capabilities for small defect detection. In the neck network, the bi-direction feature fusion idea of BiFPN is used to construct a new feature fusion network based on the improved backbone to fuse the semantic features of different layers. In addition, the Quality Focal Loss function is used to replace the cross-entropy loss function to solve the imbalance of positive and negative samples. The experimental results show that the mean average precision (mAP) of the YOLO-Xray algorithm on the CXray dataset reaches 93.5%, which is 5.1% higher than the original YOLOv5. Meanwhile, the YOLO-Xray algorithm achieves state-of-the-art detection accuracy and speed compared with other mainstream object detection models. This shows the proposed YOLO-Xray algorithm can provide technical support for bubble defect detection in chip X-ray images. The CXray dataset is also open and available at CXray.

Although space targets have different shapes, sizes and intensities, their distributions share certain commonalities. However, it is difficult to summarize a generalized distribution function for space targets. Moreover, most of the existing methods based on deep learning are not suitable to use directly because of the size of targets and the cost of manual labeling for a full image. In this paper, we proposed a pattern for space target detection based on a convolutional neural network (CNN) to learn essential features of the targets from data. In the processing stage, the background is estimated and removed. Then, image techniques are used to search and process region proposals. Different sizes of region proposals are recognized by a discriminator, which is built upon a small CNN trained with the data of several specific targets. Finally, a non-maximum suppression (NMS) operation is used to remove redundant targets. In the network structure, to further enhance the influence of the effective area, the parameters calculated from the center region of the input are utilized as guidance information and added to the features before the full connection. Moreover, the bias loss is applied to increase the weights of unique features. The experimental results demonstrate the outstanding performance of the proposed method in terms of the number of detected targets, accuracy rate and false alarm rate compared with baseline methods. In particular, the proposed method has a simple network structure and a lower computational cost which can be further promoted and implemented in actual engineering.

In the testing of chips, defect diagnostics in X-ray images of packaging chips is mainly performed by humans, which is time-consuming and inefficient. To overcome the abovementioned problems, a novel intelligent defect diagnostics system based on hybrid deep learning for chip X-ray images was proposed. The system consists of four successive stages: image segmentation and normalization, image reconstruction and defect detection, contour matching, and qualification diagnosis. The first stage is used to localize the external contours of the target chip and remove extraneous backgrounds through the improved UNet. Then, considering the variety of defects and the complexity of labeling, an unsupervised learning model is designed to reconstruct defect-free images to detect defects, which requires only normal samples for training. Third, the multicomponent template matching based on structural prior is used to localize the internal contours of the chip. In the final stage, the qualification is diagnosed based on the previous results through the Floyd–Warshall algorithm. The effectiveness and robustness of the proposed methods are verified by experiments on real-world inspection lines. The experimental results demonstrate that the developed system can successfully perform fault diagnostics tasks, achieving a judgment accuracy of 92.5%.

Integrated circuit chips are the most common electronic components, and visual internal defect detection is essential for ensuring product quality following packaging. However, efficient detection of chip internal defects is challenging due to the complexity of the background and the faintness of defects. To overcome the above difficulties, a novel deep learning-based defect segmentation framework is proposed, which relies on an image pre-processing (IPP) scheme and a defect segmentation network (DSNet). The IPP is composed of a rotation correction algorithm and a region segmentation algorithm for removing the influence of background and obtaining chip packaging region. The DSNet is proposed to precisely and efficiently segment the internal defects. To address the scarcity of data and avoid overfitting, we proposed the lightweight convolution block by using depth-wise separable convolution to reduce the number of parameters. Besides, the attention gate module is incorporated into the skip connection to handle the shape varieties of the defects. Moreover, the Focal loss function is designed to guide the network to pay attention to small defects that are difficult to distinguish. The robustness and adaptability of the proposed method are evaluated on three typical types of chip X-ray datasets from real-world inspection lines. Experimental results show that the proposed framework achieves a satisfactory trade-off between detection accuracy and speed with an F1 score of 72.69% and an FPS of 17.5 on average, resulting in superior segmentation performance even with sparse or insufficient data.

A low-profile Planar Ultra-wideband Modular antenna (PUMA) array is proposed in this paper. Tightly coupled dipole printed on substrate is used to realize ultra wide band. By tuning the shape of dipoles and the location of two shorting vias can reduce the height of the antenna profile. The proposed antenna can achieve wide impedance bandwidth ranged from 1230 GHz with VSWR < 3 (the relative bandwidth is 2.5:1) for 45-degree scans in H-plane and E-plane. What’s more, the height of the radiating layer is less than 0.16 wavelength and the whole antenna is less than 0.33 wavelength at the highest operating frequency of operation.

For the surface defects of aluminum profiles, there are problems of multi-scale, small object and irregular shape. This paper proposes a defects detection algorithm based on improved feature pyramid. This method compresses and saves the feature information extracted by the backbone networks, and calculates the similarity between deep and shallow features, so as to alleviate the phenomenon of loss of feature information and weakening of feature expression ability, thereby solving the problem of multi-scale and small object. At the same time, deformable convolution is introduced to enhance the feature extraction ability of the model and alleviate the detection problems caused by irregularly shaped defects. To verify the effectiveness of the proposed method, Faster R-CNN was used as the basic detection algorithm to conduct ablation experiments, and compared with the classical detection algorithm, the accuracy rate was as high as 72.8%. The experimental results show that the proposed method has a good performance on the task of aluminum profile defects detection, and is superior to the comparative detection algorithms.