Development Pattern Recognition Model for the Classification of Circuit Probe Wafer Maps on Semiconductors

Chang, Cheng-Wei; Chao, Tsung-Ming; Horng, Jorng-Tzong; Lu, Chien-Feng; Yeh, Rong-Hwei

doi:10.1109/tcpmt.2012.2215327

Cited by 26 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These reflections cause unwanted vertical edges on the pads in the subsequent edge detection step, and should be removed to increase solder defect detection accuracy. The reflections are detected using the circular Hough transform [8] where a cricle is represented using:…”

Section: Vertical Edge Detection Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Ieamsaard

Muneesawang

Sandnes

2018

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

The detection of low quality solder joint quality in hard disk drive (HDD) manufacturing is a time consuming, error-prone and costly process that is often performed manually. This paper thus proposes two automated optical solder jet ball joint defect inspection methods for head gimbal assembly (HGA) production. The first method uses a Support Vector Machine (SVM) for fault detection and the second method uses vertical edge detection to identify solder ball and pad burning defects. The methods were tested with 5,530 HGA images, and their performance was compared to a Bayesian-based method. Experimental results show that the vertical edge detection method gave the best results, with an under reject rate of 0.75% and an over reject rate of 1.88%. The accuracy of the vertical edge detection method was 98.2%, which is higher than the accuracy of 89.9% for the Bayesian-based method, and 84.6% for the SVM-based method. Index Terms-optical inspection; solder jet ball joint defect; vertical edge detection; HDD manufacture

show abstract

Section: Vertical Edge Detection Based Methodsmentioning

confidence: 99%

“…The locus of (a, b) points in the parameter space fall on a circle of radius R centered at (x, y). The true center point will be common to all parameter circles, and is found using a Hough accumulation array [8]- [11].…”

Section: Vertical Edge Detection Based Methodsmentioning

confidence: 99%

Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Ieamsaard

Muneesawang

Sandnes

2018

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

show abstract

“…Hence, it is important to separate random and systematic defects in the WBM since the systematic defect's signature can reveal the process problem. Therefore, it is important to detect and classify these defects, in order to identify the root causes of failure and to take appropriate actions for quality and yield enhancement [55], [58], [59], [61]. Although after generating the WM, the defective dies can be detected by an experienced human inspector; however, as this process can be costly and time consuming, many researchers evaluated the dies' quality using AOI techniques [62].…”

Section: B Semiconductor Wafer Defectsmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

“…Many papers afford quality issues using DM techniques [50][51][52][53][54][55][56][57][58][59][60][61][62][63] . Among them, semiconductor and electronics manufacturing can be considered a hot topic [51,53,58,59,61,63]. Quality problems together with new capabilities usually influence the design process of new products.…”

Section: Kdd Techniques Applied To Manufacturing Processesmentioning

confidence: 99%

A knowledge discovery in databases approach for industrial microgrid planning

Gamarra

Guerrero

Montero

2016

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Development Pattern Recognition Model for the Classification of Circuit Probe Wafer Maps on Semiconductors

Cited by 26 publications

References 21 publications

Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

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

A knowledge discovery in databases approach for industrial microgrid planning

Contact Info

Product

Resources

About