Automated post bonding inspection by using machine vision techniques

Wang, Mao‐Jiun J.; Wu, Wen‐Teng; Hsu, Chih‐Cheng

doi:10.1080/00207540210136568

Cited by 17 publications

(6 citation statements)

References 10 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many industrial inspection tasks such as textile [13], printed circuit boards (PCBs) [17], ball grid arrays (BGA) [20], passive components [19], electric components [8], wire bonding for Integrated Circuits (IC) [9,18], machine tools [5] and profile matching for machined parts [1], have used machine vision inspection to improve productivity and quality management [10,11,15]. In order to achieve high efficiency and effectiveness in production, the development of automated visual inspection systems is essential.…”

Section: Introductionmentioning

confidence: 99%

Using Eigenvalues of Covariance Matrices for Automated Visual Inspection of Microdrills

Tien

Yeh

2005

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper proposes a translation, rotation, and template-free automated visual inspection scheme that detects microdrill defects using the eigenvalues of covariance matrices. We first derived the colour images of microdrills and extracted the boundary of the first facets. Then, the smaller eigenvalues of the covariance matrices of given regions of support were calculated for boundary representation, and they were thresholded to separate the boundaries into segments. The least square linear regression method was used to fit the segments into linear equations. Eventually, the defects were detected by three inspection rules that measure five features of microdrills including: gap distance, parallel, and enclosed angles, accordingly. The proposed scheme was implemented in C++ with a graphical user interface environment. Fifteen microdrills, digitized without alignment, were used to verify the proposed inspection process. Experimental results show that the proposed scheme reliably achieves the inspection of microdrills.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Eigenvalues of Covariance Matrices for Automated Visual Inspection of Microdrills

Tien

Yeh

2005

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Recently x-ray inspection was reported in [68][69][70] in which defects such as high/low loop, missing, sagged, and broken wires were identified.…”

Section: X-raymentioning

confidence: 99%

“…In [68], a transformation and threshold segmentation method was used to locate the region of interest (ROI) containing bond wires and a geometric feature extraction mechanism was utilized. In [69,70], similar image processing algorithms were used for detecting wire bonding defects.…”

Section: Image Processingmentioning

confidence: 99%

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

Alam

Kehtarnavaz

2022

IEEE Access

View full text Add to dashboard Cite

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

show abstract

“…It has been an excellent tool for many industrial inspection tasks such as textiles [20], printed circuit boards [30], electric components [11], chip alignment, wire bonding for Integrated Circuit (IC) [12,27], machine tools [25] and profile matching for machined parts [7]. Various techniques used for these inspection applications have been reviewed by Chin [6], Newman and Jain [19], Thomas et al.…”

Section: Introductionmentioning

confidence: 98%