Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration

Liao, Guanglan; Chen, Pengfei; Du, Li; Su, Lei; Liu, Zhiping; Tang, Zirong; Shi, Tielin

doi:10.1016/j.microrel.2015.09.009

Cited by 22 publications

(6 citation statements)

References 24 publications

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“…In that way, the Kohonen family presents an unavoidable question in terms of determining the autonomous representation of the written knowledge in the Kohonen calculation. The Kohonen Map Architecture [ 64 ] is demonstrated in Fig 3 .…”

Section: Methodology and Approachmentioning

confidence: 99%

SOM-LWL method for identification of COVID-19 on chest X-rays

et al. 2021

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The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

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Section: Methodology and Approachmentioning

confidence: 99%

SOM-LWL method for identification of COVID-19 on chest X-rays

et al. 2021

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“…At the same time, each neuron is assigned with some weight value based on the input space. The SOM architecture has a feed-forward structure with a 2D computational layer of nodes arranged in rows and columns and connected fully with all other sources of the input layer [ 24 ]. Figure 3 depicts an architectural overview of Kohonen’s SOM.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath¹,

Sivakamasundari²,

Begum

2022

Wireless Pers Commun

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Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.

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“…X-ray is used to look for die attachment defects by its ability to penetrate the substrate material and detect hidden faults such as voids in die attachment [29], defective solder bumps/joints/balls in a ball greed array [30][31][32][33][34][35][36][37]. In [29], radiographic images were acquired from a semiconductor chip and then image processing methods were employed to automatically compute the amount of epoxy die attachment voids.…”

Section: X-raymentioning

confidence: 99%

“…In [31,35], defective bumps/joints were detected. In [32], an inspection system was introduced to detect missing micro bumps from x-ray images. X-ray inspection techniques for different solder joint defects including cracks, voiding, head and pillow defects, missing solder, defects in bridges were studied in [33,34,36,37].…”

Section: X-raymentioning

confidence: 99%

“…In [26], the pre-processing stage consisted of two steps of image segmentation for extracting the IC under inspection from a captured image by using a geometric feature vector. Image data acquired via x-ray often use image processing algorithms such as filtering, energy normalization, thresholding, and image enhancement for defect detection as discussed in [29][30][31][32][33][34][35][36][37]. In [38][39][40][41][42][43][44], images of flip chips captured by SAM systems were first segmented based on the flip chip structure using different segmentation techniques.…”

Section: Image Processingmentioning

confidence: 99%

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A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing

Alam

Kehtarnavaz

2022

IEEE Access

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Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the power and signal transmission quality and dependability in an IC. This paper presents a survey or literature review of the methods used for detecting these defects based on different sensing modalities used including optical, radiological, acoustical, and infrared thermography. A discussion of the detection methods used is provided in this survey. Both conventional and deep learning approaches for detecting die attachment and wire bonding defects are considered along with challenges and future research directions.INDEX TERMS die attachment and wire bonding defects, defect detection in IC manufacturing, integrated circuit (IC) defects

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Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration

Cited by 22 publications

References 24 publications

SOM-LWL method for identification of COVID-19 on chest X-rays

SOM-LWL method for identification of COVID-19 on chest X-rays

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing

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