The printed circuit board (PCB) stands as the cornerstone of electronic equipment, with its schematic holding paramount importance for system performance and reliability. In light of the pervasive use of electronic devices in society, concerns regarding maintenance, safety, backdoors, and other latent issues have garnered significant attention. Automatic schematic generation (ASG), with its distinct capability for generating circuit schematics autonomously, not only plays a pivotal role in electronic design automation (EDA) but also aids in deciphering the fundamental principles of PCB equipment to effectively address these underlying issues. However, constrained by the increasingly sophisticated manufacturing processes of PCBs and the inherent legal and ethical controversies surrounding reverse engineering, the development of related technologies faces notable bottlenecks. To break through technical barriers and advance technological progress, this paper comprehensively combs through the existing ASG, offers in-depth description of the core algorithms of the technology—layout and routing, and for the application of the technology in PCB reverse engineering, analyzes in detail the current challenges and the faced problems. Around these challenges, feasible solutions are discussed in this paper, with the aims of promoting the research of automatic PCB schematic generation technology and contributing new strength to EDA and PCB reverse engineering automation.

In Graph Convolutional Networks (GCNs), the aggregation of node features in graph convolutional learning is typically guided solely by the topology of the graphs. However, both network topology and node features provide unique and valuable information. Relying solely on topology cannot yield entirely accurate and comprehensive neighborhood information. This paper proposes Output Correction for GCNs (OC-GCN), which advocates for representation learning incorporating feature and topology views. Specifically, we employ two GCN encoders to extract node embeddings in both the feature and topology spaces. We first identify consistent and inconsistent nodes by comparing the pseudo-labels generated by the encoders. Subsequently, we regenerate representations of inconsistent nodes by aggregating the representations of consistent nodes within their respective neighborhoods. Our experiments demonstrate that OC-GCN significantly enhances the classification accuracy of inconsistent nodes. We conducted extensive experiments on benchmark datasets and observed that OC-GCN outperforms state-of-the-art baselines across various label rates.

PCB reverse engineering plays a crucial role in verifying circuit design, detecting hardware Trojans, and maintaining outdated devices. The reverse generation of PCB schematics, a vital aspect of this engineering, heavily relies on manual design due to the challenge of objectively evaluating schematic quality. This paper introduces a novel aesthetic evaluation metric to assess the quality of PCB schematics. Based on this metric, a PCB schematic reverse generation method using reinforcement learning is proposed. Experimental results demonstrate the metric and method’s reliability and effectiveness, as they can automatically generate PCB schematics comparable to those designed by human engineers.

Abstract Element detection is a key step in non‐destructive testing of printed circuit board (PCB) based on computed tomography (CT). In recent years, some image segmentation methods based on deep learning have shown great potential in the element segmentation task of PCB CT images, and greatly improved the efficiency and accuracy. However, since image segmentation is based on pixel‐level classification, the annotation of training data is difficult and costly. Aiming at this problem, the authors proposed a new active learning method based on the integration of relevant information about segmentation tasks and data variance. In this method, the Result Quality Evaluation Module (RQEM) proposed by us is used to generate task‐related information, and an adversarial network is used to generate the difference information between samples and the initial labelled data. Finally, the two parts of information are fused and used as the standard of data selecting. In the PCB CT image element segmentation task, the authors only need to select 12.7% of the whole training set with their proposed method to make Mean Intersection Over Union (MIOU) reach 79.7, which has reached 95% of the optimal performance of 83.7. The in‐depth analysis also verifies the effectiveness and stability of the authors’ method.

In image classification of deep learning, adversarial examples where input is intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them. Different attack and defense strategies have been proposed to better research the mechanism of deep learning. However, those researches in these networks are only for one aspect, either an attack or a defense. There is in the improvement of offensive and defensive performance, and it is difficult to promote each other in the same framework. In this paper, we propose Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial examples, which can learn and approximate the distribution of the original instances and adversarial examples, especially promoting attackers and defenders to confront each other and improve their ability. For CycleAdvGAN, once the Generator A and D are trained, GA can generate adversarial perturbations efficiently for any instance, improving the performance of the existing attack methods, and GD can generate recovery adversarial examples to clean instances, defending against existing attack methods. We apply CycleAdvGAN under semiwhite-box and black-box settings on two public datasets MNIST and CIFAR10. Using the extensive experiments, we show that our method has achieved the state-of-the-art adversarial attack method and also has efficiently improved the defense ability, which made the integration of adversarial attack and defense come true. In addition, it has improved the attack effect only trained on the adversarial dataset generated by any kind of adversarial attack.

Printed circuit board wire segmentation based on computed tomography (CT) image can help subsequently locate and estimate inner faults of circuit in an automatic and non‐destructive manner. However, CT imaging is prone to suffer from scattered artefacts, metal artefacts and other interference, destroying compact boundary structures of wires. Wires have the characteristic of dense local distribution, and massive vias, pads, and coppers can appear close to wires, resulting in mazy recognition surroundings. The above‐mentioned problems bring great difficulty for high‐accuracy recognition and location of wire segmentation. In this study, considering that deep convolutional neural network (DCNN) with powerful feature representation can recognise wires in confused surroundings, and graph cut (GC) model relying on grayscale and local texture information specialises in protecting edge structures of wires, the authors propose an effective framework called DCNN‐GC that employs DCNN to obtain global semantic prior to guide the GC model to accomplish satisfactory wire segmentation. The authors qualitative and quantitative results demonstrate outstanding performance, and achieve overwhelming intersection over union compared with traditional and DCNN‐based methods.

Image segmentation can be used in non-destructive testing, tracking and recognition. Level set method for image segmentation has poor performance on efficiency. In this paper, we propose to use max-flow algorithm to optimize a locally improved Chan-Vese model for image segmentation in the presence of intensity inhomogeneity. The energy function of local Chan-Vese model is introduced firstly. This model consists of global term, local term and penalty term and the local term contributes the segmentation for images with intensity inhomogeneity. Then, we convert this energy function to the frame of Graph Cut whose energy function can be efficiently minimized by max-flow algorithm. As a result, the process of optimization of local Chan-Vese model can be accelerated by using max-flow algorithm. The experiments demonstrate that the proposed method can achieve satisfactory segmentation for images with intensity inhomogeneity as well as very high efficiency.

This study proposes an improved level set method for segmenting images with intensity inhomogeneity. One of the improvements is to consider the difference between an original image and an estimated image without bias field in the image model. Apart from using this difference, Gaussian distribution with means and variance is utilised as the local intensity descriptor to map the original image into another domain so the object and the background can be better separated in the transformed domain. Then, an improved level set energy function that combines the image term, local variance, and the above difference is defined. The minimisation of the function can be processed by level set evolution. The proposed method is compared with existing methods, and experiments on both synthetic and real images demonstrate that authors’ method has superior performance.