Clustered defect detection of high quality chips using self-supervised multilayer perceptron

Huang, Chenn‐Jung

doi:10.1016/j.eswa.2006.07.011

Cited by 34 publications

(12 citation statements)

References 16 publications

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“…For performance evaluation, we have applied the neural network approach, a more sophisticated approach published before for inspection, [24][25][26] to compare with the proposed method. The neural network method decomposes an image of ͑M ϫ N͒ pixels into a set of subimages, each of which has a size of ͑m ϫ n͒ pixels and is a wavelet processing unit.…”

Section: Resultsmentioning

confidence: 99%

Automated detection of light-emitting-diode chip surface blemishes on two background textures

Lin

2008

Opt. Eng.

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This research explores the automated detection of surface blemishes that fall across two different background textures in a lightemitting-diode ͑LED͒ chip. Water-drop blemishes, commonly found on chip surfaces, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop blemish is difficult, because the blemish has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. We first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. Then, the T 2 statistic of multivariate statistical analysis is applied to integrate the multiple wavelet characteristics. Thus, the wavelet-based T 2 approach judges the existence of water-drop blemishes. Finally, we compare the defect detection performance among the wavelet-based T 2 method and traditional methods. Experimental results show that the proposed method achieves a 95.8% probability of accurately detecting the existence of water-drop blemishes, and an approximate 92.6% probability of correctly segmenting their regions. © 2008 Society of Photo-Optical Instrumentation Engineers.

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Section: Resultsmentioning

confidence: 99%

Automated detection of light-emitting-diode chip surface blemishes on two background textures

Lin

2008

Opt. Eng.

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“…For example, at the IC placing stage, defect cases of missing, wrong or doubled components may occur. In terms of possible soldering defects, most of them happen after the reflowing stage, such as the defects at the IC package components (pseudo joint, excess solder, insufficient solder, shifting Solder and bridge defects) and the defects at the non-IC components (side termination, [63] Ring, scratch, zone and repeating types [64] Ring, scratch, random and new patterns [65] Systematic and random patterns [66] Circle, cluster, scratch and spots [67] [68] Bull's Eye, Edge ring, scratch, random, multiple zones, multiple scratches, ring-zone mixed pattern and ring-scratch mixed pattern [69] [70] Multiple zones, multiple scratches, ring-zone mixed pattern and ring-scratch mixed pattern [71] [72] Cluster defects such as scratch, strains and localized failures [58] Checkerboard, ring, right-down edge, composite and random patterns [73] Spatially homogeneous Bernoulli process, cluster, circle, spot, repetitive and mixed pattern [74] Scratch, center and edge [75] Quarter ring, up and left, Quarter ring, up and right, Edge effects, Ring effects, Semi-ring, up, Semi-ring, up Edge effects, up and bottom Cluster [76] Annulus, half-annulus, band and half-ring [77]- [81] Curvilinear, amorphous, and ring [82] Linear and circular patterns [83] [84] Bull's eye, Bottom, Crescent moon, edge and random [85] Random, ring, curvilinear and ellipsoid [59] Line, edge, ring, blob and bull's eye [61] [53] Bull's eye, blob, line, edge, hat and ring [86] Multiple patterns including ring, checkerboard and five radial zones [87] Random, systematic and ,mixed patterns [88] [57] Circle, cluster, repetitive and spot [56], [60], [62], [89]-…”

Section: Pcb Defectsmentioning

confidence: 99%

“…Some examples of nonlinear filters are median filters and Prewitt filter. Median filters are popular preprocessing method for abnormality detection in electronic device inspection applications [71]. More advanced denoising techniques such as deep learning models [322], low rank approximation [323], and weighted nuclear norm minimization [324] are also used in literature.…”

Section: A Preprocessingmentioning

confidence: 99%

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

2020

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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.

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“…For example, C.H.Wang [7] designed a spatial defect diagnosis system for wafer and device manufacturing, the system can estimate the number of clusters in advance, and can separate both convex and non-convex defect clusters at the same time. C.J.Huang [8] proposed an automatic wafer-scale defect cluster identifier that uses a multilayer perceptron to detect the defect cluster and mark all of the defective dies. N.G.Shankar [9] presented a rule-based approach to detect defect and to classify the defect patterns that appear on the wafer surfaces.…”

Section: Introductionmentioning

confidence: 99%

IC wafer defect detection using image segmentation based on cultural algorithms

2008

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The integrated circuit (IC) design technology has made the chip density continue to increase. The high performance chips have a high sensitivity to the slightest defects in manufacturing procedure, therefore the defect detection is needed in order to ensure the performances of chip and semiconductor device. For the defects on a wafer, a detection method using image segmentation is presented in this paper. The method performs the segmentation of wafer image by selecting the threshold values. The optimal thresholds are computed by the technique based on cultural algorithm. The designs of population space, the belief space, and communication protocol in the cultural algorithm, are given in detail. An integration of genetic algorithm and simulated annealing is used to produce the new individuals in the population space. The experimental results show that the defect detection method proposed in this paper can obtain the features of defects effectively, the better image about the defect on a wafer can be obtained if the differential chart of the original image and the intact image of wafer is computed in advance.

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Clustered defect detection of high quality chips using self-supervised multilayer perceptron

Cited by 34 publications

References 16 publications

Automated detection of light-emitting-diode chip surface blemishes on two background textures

Automated detection of light-emitting-diode chip surface blemishes on two background textures

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

IC wafer defect detection using image segmentation based on cultural algorithms

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