Feature Extraction and Template Matching Algorithms Classification for PCB Fiducial Verification

Fonseka, C. L. S. C.; Jayasinghe, J.A.K.S.

doi:10.5604/01.3001.0011.6015

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…6. Time-pressure dispensing schematic [39] algorithms include data pre-processing, feature extraction and classification.…”

Section: B Measuring the Volume Of Glue Dotmentioning

confidence: 99%

“…Excess and Insufficient glue on the PCB joints are two types of defects that may occur during the manufacturing and assembly process. Electric tests and human visual inspection tests are common methods of detection and interpretation, but novel automated optical inspection technologies alongside modern inspection algorithms for PCB quality control are gaining impetus [38] [39].…”

Section: B Measuring the Volume Of Glue Dotmentioning

confidence: 99%

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The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Danishvar¹,

Daneshvar²,

Mousavi³

2022

Preprint

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A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the DNA, repeatable and predictable.<br>

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“…6. Time-pressure dispensing schematic [39] algorithms include data pre-processing, feature extraction and classification.…”

Section: B Measuring the Volume Of Glue Dotmentioning

confidence: 99%

Section: B Measuring the Volume Of Glue Dotmentioning

confidence: 99%

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Danishvar¹,

Daneshvar²,

Mousavi³

2022

Preprint

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“…Fonseka et al conducted 4 studies to investigate THT solder joint defects. Three of these studies [150]- [152] were focusing on extracting the features and creating color models for pad and solder joint detection. While the fourth study [148] was a continuation approach that focused on automatic solder quality classification for solder bridging, voids in drillhole region, voids on pad region, and excess solder on solder joint.…”

Section: Pcb Defectsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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“…The proposed method aims to be implemented in an AOI system for non-destructive optical inspection on die-level in a real manufacturing production line, typically in the semiconductor industry. Based on the recent literature, the various edge detection methods are either effective with long computation time [11][12][13][14] or short computation time with a trade-off of unstable accuracy and performance stability [16][17][18][19]. The main objectives of this paper are to address the research gap by proposing a die edge detection method which could achieve promising accuracy with stable performance within a short computation time.…”

Section: Figmentioning

confidence: 99%

“…It is worthwhile to remark that the template matching method [15] is of the most common methods implemented in the semiconductor industry for edge detection purposes [3,[16][17][18][19]. The template matching method can locate the edge of an object [20].…”

Section: Introductionmentioning

confidence: 99%

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

Tan

Leow

Cheor

2021

Preprint

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This paper presents an automated high-speed die edge detection method that is meant to be implemented in non-destructive die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e., less than 30 ms), provided the targeted unit-per-hour (UPH) is at 30,000. To demonstrate the applicability of the proposed method, the proposed method is validated using three production units (i.e., Production units A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two common edge detection methods that are widely implemented in the semiconductor industry nowadays (i.e., cross-correlation and normalized cross-correlation methods). The results obtained show that the proposed method is capable to perform die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units and outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed method is short, which is approximately 30 ms.

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Feature Extraction and Template Matching Algorithms Classification for PCB Fiducial Verification

Cited by 9 publications

References 13 publications

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

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