A Novel Defect Detection Algorithm for Flexible Integrated Circuit Package Substrates

Zhong, Zhiyan; Ma, Zhichao

doi:10.1109/tie.2021.3057026

Cited by 29 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, transitioning to computer vision in the traditional textile industry necessitates careful planning, strategic technology investment, and workforce training. Ensuring algorithm reliability in defect detection remains a significant research focus, addressing concerns about false positives and negatives (Zhong and Ma, 2021;Saberironaghi, Ren and El-Gindy, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Quality Control in Traditional Textile Manufacturing

Yosephine,

Hanna,

Setiawati

et al. 2024

JRSI

View full text Add to dashboard Cite

This research is centered on the practical implementation of machine learning and computer vision technologies to enhance production quality control within the traditional textile industry. The traditional textile sector, known for labor-intensive practices, has slowly adapted to digital transformation. We present a practical case study from Bandung, Indonesia, to validate the effectiveness of our approach in real-world textile manufacturing. By emphasizing machine learning and computer vision, this research narrows the gap between traditional textile practices and digitalization, offering tailored solutions for manufacturers seeking to excel in today's rapidly changing global market. The findings provide valuable insights into the challenges and opportunities of using machine learning and computer vision for production quality control in traditional textile manufacturing. The machine learning models in the study showed good accuracy, ranging from 75% to 100% under various lighting conditions in real-world textile manufacturing environments, confirming their suitability for practical quality control applications.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Quality Control in Traditional Textile Manufacturing

Yosephine,

Hanna,

Setiawati

et al. 2024

JRSI

View full text Add to dashboard Cite

show abstract

“…When the AOI result is out of the preliminarily set range, standard operation procedures (SOPs, such as re-work or scrap) can be conducted. AOI has became a mature technique and thus been widely used in various nonmicroelectronic industrial applications (e.g., packaging, drilling, and welding) [4][5][6]. In these applications, in practice, although production variations left negative impacts on the aesthetic appearance or information clarity, out-of-spec.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning based characteristic estimation method in printed circuit board production lines

Tsai

Qiu

et al. 2023

Flex. Print. Electron.

View full text Add to dashboard Cite

In this study, software and hardware that supported automatic optical inspection (AOI) for printed circuit board production line was proposed and demonstrated. The proposed method showed an effective solution that predicts off-line electromagnetic (EM) characteristic of manufactured components through in-line pattern integrity. A spiral antenna that represented complex patterns was used as the evaluation target with imitated production variations. Numerical evaluation on EM properties, batch fabrication, hardware setup and optimization, algorithm and graphical user interface development, machine learning and artificial intelligence modeling, and data verification and analysis were thoroughly conducted in this study. Results indicated that when the antenna showed pattern distortion, its passive capacitance, active intensity, and active frequency increased, decreased, and decreased, respectively. These results proved that the developed system and method overcame the inability of in-line EM measurement in conventional setup. The results also showed high estimation accuracy that was not yet achieved in the past. Compared to existing or similar AOI ideas, the proposed method supports analyses on complex pattern, provides solutions on target design, and efficient algorithm generation. This work also proved active and passive EM signals with evidences, and exhibited outstanding confidence levels for characteristic estimations. The proposed system and method indicated their potential in smart manufacturing.

show abstract

“…Currently, there is limited research on defect detection in the manufacturing processes of HDI substrates, particularly lacking effective detection methods for complex defect types such as etching defects. [7][8][9][10] High-density FICS digital images are acquired through a metallographic microscope platform, exhibiting diverse grain shapes, significant noise interference, and large variations in grayscale. 11,12 During defect detection, the texture structure of FICS images may resemble certain defects, thereby interfering with defect detection.…”

Section: Introductionmentioning

confidence: 99%

A differential geometry-based method for detecting etching defects in high-density interconnect IC substrates

Yu,

Huang,

Zhou

et al. 2023

First Advanced Imaging and Information Processing Conference (AIIP 2023)

View full text Add to dashboard Cite

A Novel Defect Detection Algorithm for Flexible Integrated Circuit Package Substrates

Cited by 29 publications

References 31 publications

Machine Learning for Quality Control in Traditional Textile Manufacturing

Machine Learning for Quality Control in Traditional Textile Manufacturing

Machine-learning based characteristic estimation method in printed circuit board production lines

A differential geometry-based method for detecting etching defects in high-density interconnect IC substrates

Contact Info

Product

Resources

About