An official website of the United States government
This paper presents an innovative solution to the challenge of part obsolescence in microelectronics, focusing on the semantic segmentation of PCB X-ray images using deep learning. Addressing the scarcity of annotated datasets, we developed a novel method to synthesize X-ray images of PCBs, employing virtual images with predefined geometries and inherent labeling to eliminate the need for manual annotation. Our approach involves creating realistic synthetic images that mimic actual X-ray projections, enhanced by incorporating noise profiles derived from real X-ray images. Two deep learning networks, based on the U-Net architecture with a VGG-16 backbone, were trained exclusively on these synthetic datasets to segment PCB junctions and traces. The results demonstrate the effectiveness of this synthetic data-driven approach, with the networks achieving high Jaccard indices on real PCB X-ray images. This study not only offers a scalable and cost-effective alternative for dataset generation in microelectronics but also highlights the potential of synthetic data in training models for complex image analysis tasks, suggesting broad applications in various domains where data scarcity is a concern.
more »
« less
Via Modeling on X-Ray Images of Printed Circuit Boards Through Deep Learning
A widely-regarded approach in Printed Circuit Board (PCB) reverse engineering (RE) uses non-destructive Xray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. The noise sources specific to X-ray CT and variability from designers make it difficult to acquire the features needed for the RE process. Hence, these X-ray CT images require specialized image processing techniques to examine the various features of a single PCB to later be translated to a readable CAD format. Previously, we presented an approach where the Hough Circle Transform was used for initial feature detection, and then an iterative false positive removal process was developed specifically for detecting vias on PCBs. Its performance was compared to an off-the-shelf application of the Mask Region-based Convolutional Network (M-RCNN). M-RCNN is an excellent deep learning approach that is able to localize and classify numerous objects of different scales within a single image. In this paper, we present a version of M-RCNN that is fine-tuned for via detection. Changes include polygon boundary annotations on the single X-ray images of vias for training and transfer learning to leverage the full potential of the network. We discuss the challenges of detecting vias using deep learning, our working solution, and our experimental procedure. Additionally, we provide a qualitative evaluation of our approach and use quantitative metrics to compare the proposed approach with the previous iterative one.
more »
« less
- Award ID(s):
- 1821780
- PAR ID:
- 10230900
- Date Published:
- Journal Name:
- GOMACTech
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Micro-CT, also known as X-ray micro-computed tomography, has emerged as the primary instrument for pore-scale properties study in geological materials. Several studies have used deep learning to achieve super-resolution reconstruction in order to balance the trade-off between resolution of CT images and field of view. Nevertheless, most existing methods only work with single-scale CT scans, ignoring the possibility of using multi-scale image features for image reconstruction. In this study, we proposed a super-resolution approach via multi-scale fusion using residual U-Net for rock micro-CT image reconstruction (MS-ResUnet). The residual U-Net provides an encoder-decoder structure. In each encoder layer, several residual sequential blocks and improved residual blocks are used. The decoder is composed of convolutional ReLU residual blocks and residual chained pooling blocks. During the encoding-decoding method, information transfers between neighboring multi-resolution images are fused, resulting in richer rock characteristic information. Qualitative and quantitative comparisons of sandstone, carbonate, and coal CT images demonstrate that our proposed algorithm surpasses existing approaches. Our model accurately reconstructed the intricate details of pores in carbonate and sandstone, as well as clearly visible coal cracks.more » « less
-
null (Ed.)X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.more » « less
-
The security of printed circuit boards (PCBs) has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. While detecting tampering on a PCB is the first step for verification, forensics is also needed to identify the modified component. One non-invasive and reliable PCB tamper detection technique with global coverage is the impedance characterization of PCB's power delivery network (PDN). However, it is an open question whether one can use the two-dimensional impedance signatures for forensics purposes. In this work, we introduce a novel PCB forensics approach, using explainable AI (XAI) on impedance signatures. Through extensive experiments, we replicate various PCB tamper events, generating a dataset used to develop an XAI algorithm capable of not only detecting tampering but also explaining why the algorithm makes a decision about whether a tamper event has happened. At the core of our XAI algorithm is a random forest classifier with an accuracy of 96.7%, sufficient to explain the algorithm's decisions. To understand the behavior of the classifier In the decision-making process, we utilized the SHAP values as an XAI tool to determine which frequency component influences the classifier's decision for a particular class the most. This approach enhances detection capabilities as well as advancing the verifier's ability to reverse-engineer and analyze two-dimensional impedance signatures for forensics.more » « less
-
Image security is becoming an increasingly important issue due to advances in deep learning based image manipulations, such as deep image inpainting and deepfakes. There has been considerable work to date on detecting such image manipulations using improved algorithms, with little attention paid to the possible role that hardware advances may have for improving security. We propose to use a focal stack camera as a novel secure imaging device, to the best of our knowledge, that facilitates localizing modified regions in manipulated images. We show that applying convolutional neural network detection methods to focal stack images achieves significantly better detection accuracy compared to single image based forgery detection. This work demonstrates that focal stack images could be used as a novel secure image file format and opens up a new direction for secure imaging.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
