Character Identification for Integrated Circuit Components on Printed Circuit Boards Using Deep Learning

2021 ◽  
Author(s):  
Xiaojun Jia ◽  
Zihao Liu
Keyword(s):  
Deep Learning ◽  
Integrated Circuit ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Character Identification ◽  
Circuit Components

scholarly journals Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once

2021 ◽  
Vol 18 (4) ◽  
pp. 4411-4428
Author(s):  
Venkat Anil Adibhatla ◽  
◽  
Huan-Chuang Chih ◽  
Chi-Chang Hsu ◽  
Joseph Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit

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.

Building block programs for the layout of printed circuit boards utilizing integrated circuit packs (DAPSYS V.2)

1968 ◽  
Author(s):  
Robert W. Kadis ◽  
Kenneth L. Thompson ◽  
William J. Volkman ◽  
W. Lawrence Hill ◽  
Charlotte E. Gillette
Keyword(s):  
Integrated Circuit ◽  
Building Block ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit

scholarly journals A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

2021 ◽  
Vol 7 ◽  
Author(s):  
Ameya Kulkarni ◽  
Chengying Xu
Keyword(s):  
Deep Learning ◽  
Supply Chains ◽  
Printed Circuit Boards ◽  
Learning Approach ◽  
Hardware Trojan ◽  
Circuit Boards ◽  
Hardware Trojans ◽  
Research Attention ◽  
Printed Circuit ◽  
Optical Inspection

Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipment. Security compromise or data theft can have severe impact and thus demand research attention. The advantage of the proposed method is that it can be implemented in a manufacturing environment with limited training data. It can also provide better coverage in detection of hardware trojans over traditional methods. Image recognition algorithms need to have deeper penetration inside the training layers for recognizing physical variations of image patches. However, traditional network architectures often face vanishing gradient problem when the network layers are added. This hampers the overall accuracy of the network. To solve this a Residual network with multiple layers is used in this article. The ResNet34 algorithm can identify manufacturing tolerances and can differentiate between a manufacturing defect and a hardware trojan. The ResNet operates on the fundamental principle of learning from the residual of the output of preceding layer. In the degradation issue, it is observed that, a shallower network performs better than deeper network. However, this is with the downside of lower accuracy. Thus, a skip connection is made to provide an alternative path for the gradient to skip forward the training of few layers and add in multiple repeating blocks to achieve higher accuracy and lower training times. Implementation of this method can bolster automated optical inspection setup used to detect manufacturing variances on a printed circuit board. The results show a 98.5% accuracy in optically detecting trojans by this method and can help cut down redundancy of physically testing each board. The research results also provide a new consideration of hardware trojan benchmarking and its effect on optical detection.

Recycling of printed circuit boards by robot manipulator: A Deep Learning Approach

2021 ◽  
Author(s):  
Kohei Naito ◽  
Aya Shirai ◽  
Shin-ichiro Kaneko ◽  
Genci Capi
Keyword(s):  
Deep Learning ◽  
Printed Circuit Boards ◽  
Robot Manipulator ◽  
Learning Approach ◽  
Circuit Boards ◽  
Printed Circuit
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