PCB solder pad inspection mechanism using gerber file

Raghuvanshi, Vediya; Burman, Aparajita; Bartakke, Prashant; Deshpande, Satish P.

doi:10.1109/iccsp.2016.7754367

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The flow process of the board re-spin methodology from software to hardware is presented in Figure 4. The first step is the schematic entry followed by the design layout of the PCB into a Gerber file [23], [24]. The Gerber file will be sent to the board fabrication houses to fabricate and assemble the board and its components.…”

Section: The Flow Processesmentioning

confidence: 99%

Mitigating board endorsement through re-spinning with surface-mounted device under test pad

Malimban

Ortiz

Enojas

2022

IJEECS

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The two-part legacy board device under test (DUT) pad has been very useful for testing of microelectronic packages. However, <span lang="EN-US">this board has been subjected to many repair endorsements due to open connection of DUT pad and cut traces of the board components as recorded. Board re-spin is used to improve the performance of the test boards in microelectronic packages production. It improves the board development to create more functional printed circuit boards for testing and introduces the use of modern tools and software applied to the schematic creation up to the production board debugging. This paper presents a method of creating test boards for the development and manufacturing of microelectronic packages and integrated circuits. The two-part board is converted into a single board interface which eliminates the open DUT connections. A surface-mounted DUT pad is introduced to replace the old hypertact pins which are included in the re-spin board for a more robust performance. The schematic creation and board debugging were done to lessen problems caused by connection faults. In result, producing re-spin boards mitigates board endorsement which in return increases the manufacturing output performance. A faster and convenient setup was achieved when the old two-board setup was </span>eliminated.

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Section: The Flow Processesmentioning

confidence: 99%

Mitigating board endorsement through re-spinning with surface-mounted device under test pad

Malimban

Ortiz

Enojas

2022

IJEECS

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“…Based on white light reflection and digital microscope capture [3][4][5][6][7][8], there are different proposed method using digital image processing and some other machine learning techniques. For example, the YOLO is the real-time object detection that applies to detect the solder joint defects [3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the Convolution neural network (CNN) is another option for object classification. According to the research of Shijia Gao et al [5], the solder images were categorized into two classes as normal and abnormal. Then, the Support vector machine classifier (SVM) with the feature of Histogram of Oriented Gradient (HOG) was compared with the CNN solution.…”

Section: Introductionmentioning

confidence: 99%

Defect Solder Classification in Print Circuit Boards using Machine Learning

Tangsuksant,

Inthiam,

Silangren

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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This research proposes the solder inspection using the digital image processing technique and machine learning base with our machine vision prototype. There are five classes of classifying solder, including acceptable, short circuit, insufficient, blow hole and too much of solder. Automatic Optical Inspection (AOI) is used for the light source in the designed prototype and industrial camera which are installed on the mini-CNC. For the algorithm, this research applies the scanning line of binary image for detecting short circuit defection and the Random Forrest model for classifying other defects. According to the experiments, the system can classify the defect types for two classes (acceptable and unacceptable types) and five classes as 89% and 71% of accuracy, respectively.

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“…The inspection of PCBs relies on the data archived in the library. It is time consuming and considered a downtime when the parameters did not match with the PCB designed and developed which can turn out into waste [13]- [15]. Visual component libraries play a vital role for optimized designs minimizing errors and cost in PCB development [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Electronic datasheet parameter extractor and verifier system for printed circuit board development

Cuntapay

Enojas

Bongao

2022

IJEECS

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Library components are composed of devices, symbols, and footprints which are the fundamental parts to design and develop printed circuit boards (PCB). <span lang="EN-US">In archiving PCB libraries, the creation and checking of the component’s parameters are very important. The checking of footprints and pin configurations from datasheets are manually done which causes delays and downtime when the components do not fit the developed PCB. It was found in a survey that 61.5% of errors come from footprints dimensions. These cause delays in the production and development of PCBs. In this study, a parameter extractor and verifier system were developed to mitigate the error of manual process of archiving library parameters from datasheets. Text recognition and pattern matching algorithms were used for the processing of images from datasheets. The 10-trial tests conducted for each footprint for the extraction of text from the cropped images for standard operating procedures (SOP) series and heat sink thin shrink small outline package (HTSSOP) series were found successful without any error. The checking of footprints and pin configuration was reduced from 45 minutes to 25 minutes. The developed system was evaluated based on user perception by 10 PCB library users which resulted to agree that the system is functional, efficient, and convenient to use.</span>

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PCB solder pad inspection mechanism using gerber file

Cited by 5 publications

References 4 publications

Mitigating board endorsement through re-spinning with surface-mounted device under test pad

Mitigating board endorsement through re-spinning with surface-mounted device under test pad

Defect Solder Classification in Print Circuit Boards using Machine Learning

Electronic datasheet parameter extractor and verifier system for printed circuit board development

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