Automatic Optical Inspection of Soldering

Janóczki, Mihály; Becker, Ákos; Jakab, László; Gróf, Richárd; TiborTakács,

doi:10.5772/51699

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…In the past several decades, there have been several advancements in the eld of quality assessment of microelectronic components [19][20][21][22][23][24][25] . This includes methods such as automatic optical inspection (AOI) 26 , scanning electron microscopy (SEM) 27 , emission microscopy (EMMI) 28 , X-ray computed tomography (XCT) 29 , transmission electron microscopy (TEM), electrical characterization techniques, scanning acoustic microscope (SAM) and many more 30,31 .…”

Section: Introductionmentioning

confidence: 99%

Fast In-Line Failure Analysis of Sub-micron-sized Cracks in 3D Interconnect Technologies Utilizing Acoustic Interferometry

Brunner,

Paulachan,

Hammer

et al. 2023

Preprint

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More than Moore technology is driving semiconductor devices towards higher complexity and further miniaturization. Device miniaturization strongly impacts failure analysis (FA), since it triggers the need for non-destructive approaches with high resolution in combination with cost and time efficient execution. Conventional scanning acoustic microscopy (SAM) is an indispensable tool for failure analysis in the semiconductor industry, however resolution and penetration capabilities are strongly limited by the transducer frequency. In this work, we conduct an acoustic interferometry approach, based on a SAM-setup utilizing 100 MHz lenses and enabling not only sufficient penetration depth but also high resolution for efficient in-line FA of Through Silicon Vias (TSVs). Accompanied elastodynamic finite integration technique-based simulations, provide an in-depth understanding concerning the acoustic wave excitation and propagation. We show that the controlled excitation of surface acoustic waves extends the contingency towards the detection of nm-sized cracks, an essential accomplishment for modern FA of 3D-integration technologies.

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Section: Introductionmentioning

confidence: 99%

Fast In-Line Failure Analysis of Sub-micron-sized Cracks in 3D Interconnect Technologies Utilizing Acoustic Interferometry

Brunner,

Paulachan,

Hammer

et al. 2023

Preprint

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“…To detect structural defects at an early stage in the PCBA manufacturing process is necessary to reduce the PCBA production cost. Many complex and high-cost techniques have been proposed in the industry, such as using X-ray, optical, ultrasonic, and thermal imaging [6]. Using classical image processing algorithms, Automated Optical Inspection (AOI), also known as Automated Visual Inspection (AVI) was proposed as a technique that improved diagnostic capabilities in terms of speed and tasks.…”

Section: Introductionmentioning

confidence: 99%

The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Akhtar

2022

HighTech. Innov. J.

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Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PDF

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“…To implement the AOI systems for different objects, the inspection method for AOI systems needs to be adjusted based on the characters of the object. Nowadays, many AOI systems have been successfully used in real assembling lines to inspect component placement, electronic components (Kuo et al, 2019) and solder paste printing (Jan oczki et al, 2013) at the end of the SMT process. However, only a few studies have focused on inspection of solder joints especially IC solder joints owing to the challenges of their small sizes and little distinct appearance differences between qualified and unqualified ones (Cai et al, 2015;Wu and Zhang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Chen

Cai

Wang

et al. 2020

SSMT

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Purpose Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects. Design/methodology/approach In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance. Findings Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods. Originality/value The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

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Automatic Optical Inspection of Soldering

Cited by 16 publications

References 38 publications

Fast In-Line Failure Analysis of Sub-micron-sized Cracks in 3D Interconnect Technologies Utilizing Acoustic Interferometry

Fast In-Line Failure Analysis of Sub-micron-sized Cracks in 3D Interconnect Technologies Utilizing Acoustic Interferometry

The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

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