scholarly journals Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Mukhil Azhagan Mallaiyan Sathiaseelan ◽  
Olivia P. Paradis ◽  
Shayan Taheri ◽  
Navid Asadizanjani
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Communities ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Single Shot ◽  
Limited Data ◽  
Detection Techniques ◽  
Printed Circuit

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

scholarly journals Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2921
Author(s):  
Sumyung Gang ◽  
Ndayishimiye Fabrice ◽  
Daewon Chung ◽  
Joonjae Lee
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Printed Circuit Boards ◽  
Printed Circuit Board ◽  
Environmental Changes ◽  
Original Data ◽  
Circuit Board ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Proposed Model

As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

A novel convolutional neural network for electronic component classification with diverse backgrounds

2021 ◽  
Author(s):  
Longfei Zhou ◽  
Lin Zhang
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Printed Circuit Board ◽  
Manufacturing Industries ◽  
Circuit Board ◽  
Electronics Manufacturing ◽  
Electronic Components ◽  
Printed Circuit ◽  
Single Category

The rapid development of computer vision techniques has brought new opportunities for manufacturing industries, accelerating the intelligence of manufacturing systems in terms of product quality assurance, automatic assembly, and industrial robot control. In the electronics manufacturing industry, intensive variability in component shapes and colors, background brightness, and visual contrast between components and background results in difficulties in printed circuit board image classification. In this paper, we apply computer vision techniques to detect diverse electronic components from their background images, which is a challenging problem in electronics manufacturing industries because there are multiple types of components mounted on the same printed circuit board. Specifically, a 13-layer convolutional neural network (ECON) is proposed to detect electronic components either of a single category or of diverse categories. The proposed network consists of five Convolution-MaxPooling blocks, followed by a flattened layer and two fully connected layers. An electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON, Xception, VGG16, and VGG19. In this dataset, there are 11 categories of components as well as their background images. Results show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.

Characteristics of Radiated Emission from a Printed Circuit Board with a Slit

2012 ◽  
Vol 132 (6) ◽  
pp. 404-410 ◽  
Author(s):  
Kenichi Nakayama ◽  
Kenichi Kagoshima ◽  
Shigeki Takeda
Keyword(s):  
Printed Circuit Board ◽  
Circuit Board ◽  
Printed Circuit ◽  
Radiated Emission

Confirming the Signal Integrity in Transmission of Digital Signals on Microstrip Straight Circuits via the Eye Diagrams

2014 ◽  
Vol 5 (1) ◽  
pp. 737-741
Author(s):  
Alejandro Dueñas Jiménez ◽  
Francisco Jiménez Hernández
Keyword(s):  
Integrated Circuits ◽  
Printed Circuit Board ◽  
Signal Integrity ◽  
High Volume ◽  
Information Storage ◽  
Circuit Board ◽  
Electromagnetic Simulation ◽  
Electronic Systems ◽  
Digital Signals ◽  
Printed Circuit

Because of the high volume of processing, transmission, and information storage, electronic systems presently requires faster clock speeds tosynchronizethe integrated circuits. Presently the “speeds” on the connections of a printed circuit board (PCB) are in the order of the GHz. At these frequencies the behavior of the interconnects are more like that of a transmission line, and hence distortion, delay, and phase shift- effects caused by phenomena like cross talk, ringing and over shot are present and may be undesirable for the performance of a circuit or system.Some of these phrases were extracted from the chapter eight of book “2-D Electromagnetic Simulation of Passive Microstrip Circuits” from the corresponding author of this paper.

Root Cause Analysis of a Connector Time-Delayed Fracture

2015 ◽  
Author(s):  
Prabjit Singh ◽  
Ying Yu ◽  
Robert E. Davis
Keyword(s):  
Printed Circuit Board ◽  
Ceramic Substrate ◽  
Circuit Board ◽  
Delayed Fracture ◽  
Ceramic Substrates ◽  
Printed Circuit ◽  
Root Cause ◽  
Chip Carrier ◽  
Electrical Connections ◽  
Grain Boundary Grooves

Abstract A land-grid array connector, electrically connecting an array of plated contact pads on a ceramic substrate chip carrier to plated contact pads on a printed circuit board (PCB), failed in a year after assembly due to time-delayed fracture of multiple C-shaped spring connectors. The land-grid-array connectors analyzed had arrays of connectors consisting of gold on nickel plated Be-Cu C-shaped springs in compression that made electrical connections between the pads on the ceramic substrates and the PCBs. Metallography, fractography and surface analyses revealed the root cause of the C-spring connector fracture to be plating solutions trapped in deep grain boundary grooves etched into the C-spring connectors during the pre-plating cleaning operation. The stress necessary for the stress corrosion cracking mechanism was provided by the C-spring connectors, in the land-grid array, being compressed between the ceramic substrate and the printed circuit board.

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