A hybrid defect detection for in-tray semiconductor chip

Chen, Chin‐Sheng; Huang, Chien-Liang; Yeh, Chun‐Wei

doi:10.1007/s00170-012-4149-5

Cited by 7 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Content may change prior to final publication. [118] Overall quality inspection of wafer [119] IC wafer contamination [120] Micropipes defects [121]- [123] Chip-out, bridging, metal lifting, glassiviation and peel off [124] Wafer topside scratch, foreign material, ink residue, pad damage, passivation/metal damage, ink smeary, and passivation covering [125] Pinhole defects [126] Protrusion, dent, flat and bumpy defects [127] Hole, Protruding and flat patterns [128] Particles, contamination and scratches [129] Defects between line edges [130] Hole, flaw and scratch defects [131] Alignment, probe marks and bump defects for in-tray semiconductor chip [132] Spots, scratches, and bruises [133] Bond pad discoloration [54] Die edge, die street and determination of chipping size and shape [134] Spot, rock-shaped particle, ring-shaped particle, misalignment and scratch [135] Defects are classified as small, medium and large overall functionality of the circuit will be affected. Excess solder joint can cause bridging with other PCB solder joints which can lead to a short circuit.…”

Section: Pcb 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

Section: Pcb 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

show abstract

“…A single inspection approach is inadequate for inspecting the complicated surface of an inspected object. To increase the robustness of an inspection system, Chen et al 6 proposed a hybrid approach to detect defects on a complicated surface. They divided the chip surface into two parts: the circuit and bump parts.…”

Section: Related Workmentioning

confidence: 99%

“…Studies have already proposed several AOI methods for different industrial products. [2][3][4][5][6][7] These methods can be divided into five categories: the intensity-based, gradient-based, frequency-based, learning-based, and hybrid approaches. In general, operational errors during product assembly lead to product defects.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, a hybrid inspection method has been proposed. 6,7 Instead of using only one approach to inspect product quality, two or more of the aforementioned inspection methods are combined to improve the efficiency of the inspection task. A hybrid strategy is also applied to detect defects when using a compact camera module because this approach can suffer from various defects and problems including defocus, black/white dots, color defects, line defects, and dim defects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid defect detection method for compact camera lens

Chang

Chen

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Production technology has increased rapidly with the development of industrial technology. Conventional human visual inspection is insufficient for conducting quality control under this increased capacity. Therefore, high-speed and high-accuracy automated optical inspection is becoming increasingly crucial. In this article, we propose an automated inspection method for a compact camera lens using a circle Hough transformation, weighted Sobel filter, and polar transformation. Our analysis of defects in the compact camera lens identified problems including of the circular texture and the non-fixed position of the inspection region. To overcome these problems, we design an inspection algorithm for locating and inspecting a circular region. A machine learning support vector machine method is then applied for obtaining a precise detection result. The experimental results show that the proposed inspection method is suitable for detecting defects in a complicated circular inspection region, and that the proposed system exhibited high performance.

show abstract

“…BLOB analysis plays a key role in many vision systems, such as character recognition [8]- [9], traffic tracking [10], agricultural monitoring [11], face detection [12], and defect inspection [13]. Presently, owing to complex connectivity of the foreground region, most existing BLOB analysis algorithms work inefficiently in the context of machine vision.…”

Section: A Algorithm Overviewmentioning

confidence: 99%

Feature-Based Object Location of IC Pins by Using Fast Run Length Encoding BLOB Analysis

Zhong

Chen

Zhang

et al. 2014

IEEE Trans. Compon., Packag. Manufact. Technol.

View full text Add to dashboard Cite

To speed up the object location for the IC packaging inspection, this paper develops a novel binary large object (BLOB) analysis algorithm by using run length encoding (RLE). First, the new data structures for RLE and BLOB-linked lists are designed to accelerate the access and modifications of data. Second, to avoid the traditional labeling conflicts and simplify the comparisons of connectivity branches, an efficient algorithm for BLOB analysis is presented. Furthermore, area feature of objects can be extracted when the BLOB-linked list are dynamically created or modified. Finally, to evaluate the performance of the proposed method, ICs with various types of small outline packaging packages were located by the proposed algorithm. The experimental results not only demonstrate that the IC pins could be effectively and robustly located with the proposed algorithm, but also show that the presented algorithm runs faster than other classical methods.Index Terms-Binary large object (BLOB) analysis, feature extraction, integrated circuit (IC), object location, run length encoding (RLE).

show abstract

A hybrid defect detection for in-tray semiconductor chip

Cited by 7 publications

References 17 publications

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

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

A hybrid defect detection method for compact camera lens

Feature-Based Object Location of IC Pins by Using Fast Run Length Encoding BLOB Analysis

Contact Info

Product

Resources

About