Application of Decision Tree and Support Vector Machine for Inspecting Bubble Defects on LED Sealing Glue Images

Transcription

1 66 Applicatio of Decisio Tree ad Support Vector Machie for Ispectig Bubble Defects o LED Sealig Glue Images * Chua-Yu Chag ad Yi-Feg Li Abstract Bubble defect ispectio is a importat step i light-emittig diode (LED) packagig processes. It is difficult to detect bubble defects because bubbles are trasparet ad have irregular shapes i LED sealig glue images. Traditioally, experieced egieers visually check defect regios ad exclude defective elemets. However, the ispectio processes are costly ad time-cosumig. This paper proposes a automatic bubble defect ispectio method for LED sealig glue images. The system automatic locates the sealig glue regio. A decisio tree is adopted to outlie regios of iterest (ROI), ad may textural ad geometric features are extracted from the ROIs. A support-vector-machie (SVM) is used to classify the ROIs as acceptable or defective. Experimetal results demostrate effectiveess of the proposed approach. Keywords: light-emittig diode, defect ispectio, decisio tree, support-vector-machie 1. Itroductio Sice the ivetio of the lightig equipmet, lightig has become a idispesable product. LED is a semicoductor device that emits visible light. Compared with traditioal illumiatig devices, the features of LED have smaller volume, lower power requiremets, higher efficiecy, ad a loger lifetime. Figure 1 shows a LED product ad its compoet structure. There are may types of defects i the silicoe ecapsulat, such as short shot, flash, damage, ad bubble. However, ispectig LED surface defects by huma eyes is iefficiet. I idustrial maufacturig, most defects ca be detected by usig the automatic detectio system. There have bee may methods proposed for defect ispectio [1-4]. Li et al. applied a wavelet-based algorithm for the ispectio of multicrystallie solar images [1]. They used the wavelet coefficiets to distiguish local defects ad also to ehace the discrimiat features usig the wavelet s eergy differeces i two cosecutive decompositio levels. Liu et al. proposed a defect detectio method based o two-dimesioal discrete wavelet trasformatio []. The defects were detected by comparative recostructed stadard images ad test images. This method aligmet ad structure affect the results. I additio, a icorrect stadard wafer image may result i ispectio mistakes. Li et al. proposed a level-set method for LED wafer ispectio [3]. They utilized the zero-level cotours for segmetig wafers. Defects were detected by textural ad geometric features. However, they are rather complex ad time-cosumig processes. Chiou applied the decisio tree to select a proper image segmetatio method for detectig defects [4]. However, the proposed detectio method is oly suitable i a simple image. *Correspodig Author: Chua-Yu Chag ( chuayu@yutech.edu.tw) 1 Departmet of Computer Sciece ad Iformatio Egieerig, atioal Yuli Uiversity of Sciece ad Techology, 13, Sec.3, Uiversity Rd., Douliou, Yuli, 640, Taiwa

2 67. Bubble Defect Ispectio Figure shows the flowchart of the proposed method. There are four major steps i the proposed method. Details are described as follows: LED Sealig Glue Images Preprocessig Image Segmetatio (Silicoe area) Figure 1: LED product ad Basic LED structure. Most methods are based o the aalysis of the differeces of textural ad geometric features. Sice LED sealig glue images have a large area to be ispected, a system requires a log time to calculate the textural features for each pixel. However, it is difficult to detect the limpid ad icostat bubble defects. Therefore, this paper develops a method to detect bubble defects by geometric features i LED sealig glue images. Sice the LED silicoe regio is a smooth surface, it is importat to build a sigle ad uiform light source capture eviromet to reduce oises. Before applyig the algorithm, first, the silicoe ecapsulat was segmeted, which is a trasparet layer o the LED structure. ext, decisio tree is applied to aalyze bubble, edge ad backgroud regio to obtai the potetial defect regios. The features of potetial defect regios were extracted, ad the these features are classified by the SVM. Fially, the positios of the bubble are represeted. This paper is orgaized as follows: I Sectio, the approach for LED bubble defect ispectio is described, icludig preprocessig, ROI extractio, feature extractio, ad classificatio. Sectio 3 demostrates the experimetal results. Fially, coclusios are give i Sectio 4. Potetial Defect Detectio Feature Extractio Classificatio Result Figure : The flowchart of the proposed method.1 Preprocessig As show i Figure 1, a LED sealig glue image cosists of three compoets: the backgroud, the outer package regio ad the silicoe regio. Sice bubble defects occur oly i the silicoe regio, the purpose of the preprocessig is to segmet the silicoe regio. The brightess of a LED sealig glue image ca be divided ito two parts. The darker area is backgroud; ad the lighter area is LED. Therefore, Otsu s method is used to separate the LED sealig glue images ito backgroud ad LED regios [5]. Otsu s method is capable of fidig a optimal threshold value from the distributio of a image. The algorithm fids a optimal threshold value to divide a LED sealig glue image ito two groups, while the sum of variaces of the two groups is the miimum. The optimal threshold is defied as the weighted sum of variaces of the two groups: t opt arg mi w1 ( t) 1( t) w ( t) ( t) (1) t

3 68 where w 1 ad w are the probabilities of the two groups separated by t; σ 1 ad σ are the stadard deviatios of the two groups; t [0,L], ad L is the image level. Figure 3 shows a origial LED image. Figure 3 is the thresholdig result by Otsu s method. Sice the white regios i the segmeted LED image are ot coected to the backgroud regio, it is easy to extract the complete backgroud regio by meas of coected-compoet labelig. Figure 3(c) shows the segmeted backgroud regio. Based o the segmeted LED regio, we ca obtai the cetroid of LED. Ad the extract the outer package regio. The outer package regio is a dout-like circle with radius from 10 to 160 pixels. Figure 3(d) shows the cetroid of the LED ad the outer package regio. Ada _ Th( x, m / m / im / jm / f ( x i, y j) m m t () where f(x, is the gray itesity of pixel (x,. If the pixel itesity is lower tha the threshold value, the it is set to 0, otherwise it is set to 1; that is: 1, f ( x, Ada _ Th( x, Out ( x, (3) 0, f ( x, Ada _ Th( x, Figure 4 shows the segmeted regios by adaptive thresholdig. I order to obtai completely segmetatio results, a morphological closig operatio with a 9 9 structurig elemet is applied. The the resultig image is coverted egatively as show i Figure 4. Figure 4(c) shows the outer package regio ad parts of the silicoe regio. The two regios are ot coected. Figure 4(d) is the egative of Figure 4(c), ad it excludes the backgroud regio. A complete silicoe regio is obtaied by removig the o-silicoe regio ad fillig holes. Figures 4(e) ad 4(f) show the detected silicoe regio ad the silicoe regio i the origial image. (c) (d) Figure 3: Backgroud segmetatio: the origial image; result of Otsu s method; (c) detected backgroud, ad (d) LED cetroid ad outlied outer package regio. (c) Sice the silicoe regio caot be determied by a threshold value, the adaptive thresholdig method is adopted to segmet silicoe regio. The adaptive thresholdig method ca suppress the ifluece of illumiatio [6]. The adaptive threshold value is computed as: (d) (e) (f) Figure 4: Adaptive thresholdig result the egative of ; (c) outer package regios; (d) the egative of (c); (e) the silicoe regio, ad (f) the silicoe regio i origial image.

4 69. Subjective Characteristics of Elderly Speech The bubble defects are circular ad trasparet with differet sizes. Accordig to the property of bubbles, we ca use the decisio tree to classify them ito three classes: 1) edge, ) iterior of bubble ad 3) backgroud. A decisio tree is a method to represet iformatio by a machie learig algorithm [7]. All the traiig patters are outlied maually by experieced egieers. Four features are the extracted by usig a slidig widow from each traiig patter: 1). M(x, : the mea of pixel itesity i the widow. ). Std(x, : the stadard deviatio of pixel itesity i the widow. 3). R(x, : the raked positio of the ceter pixel i the widow. 4). FR(x, : the red compoet of the ceter pixel i the widow. I the traiig phase, each class selected 900 patters for traiig ad 300 patters for testig. After the traiig process, the decisio tree model was obtaied as show i Figure 5. Figure 6 shows the classificatio results of Figure 4(f) by decisio tree. Figure 5: Result of decisio tree (c) Figure 6: Classificatio of the silicoe regio: the edge regios; the iterior of bubble regios ad (c) the LED backgroud regios. By observig the iterior of bubbles, we could exclude o-bubble defect regios. The miimum boudig rectagle (MBR) is the smallest rectagle which ecloses potetial defect regios completely. Yag proposed a fast calculatio of a miimum area exterior rectagle method [8]. The method is used to determie the spidle, which is the iertia accordig to the rotary iertia of a object. MBR ca be obtaied by usig image rotatio with the edge ad spidle of the object. Therefore, we elimiated the regios i which the MBR aspect ratio is greater tha.5, ad the legth of a loger side is greater tha 55 pixels.

5 70 Figure 7 shows the locatios of potetial defects, which had excluded o-bubble defect regio. Therefore, the potetial defect regios extracted correspod to these iteral of bubble regios, as the gree rectagles show i Figure 7. The potetial defect regios will be processed i ext step. Sice bubble defects are cocetrated i a small rage of the vertical projectio. A factor P is calculated to evaluate the cocetratio of the vertical projectio P R i1 50 ( P V(i) P V(i) ) r (7) I geeral, the bubble defects have small value of P. Figure 7: Potetial defects ad potetial defect regios (a1).3 Feature Extractio The purpose of feature extractio is to obtai features to discrimiate betwee defects ad o-defects. I order to ehace performace of the idetificatio, the potetial defect regios are trasformed to the polar coordiate system. The polar coordiates (r, θ) ca be calculated from the Cartesia coordiates by: r (Yp Y ) 1 o (X p X o ) (Yp Yo ), θ ta (4) (X X ) where (X o, Y o ) is the origi, (X p, Y p ) is the positio i the Cartesia coordiates. Figures 8 -(d) show the trasformatio results. The horizotal axis is the agle [0, π], ad the vertical axis is the radius distace. Obviously, the bubble defects ad ormal regios i a polar coordiates system are differet.. I geeral, bubble defects have a similar appearace. Let P H ad P V deote the horizotal projectio ratio ad vertical projectio ratio, respectively. The P H ad P V are defied as 1 P H ( ) R r i1 B( i, ) p,for 0,1,..,359 (5) o (b1) (c1) (d1) (c) (d) Figure 8: (a1)-(d1) Potetial defect regios, ad -(d) Potetial defect regios at the polar coordiate..4 Ispectio Algorithm The similarity measuremet was applied to compare the feature of horizotal projectio ratio betwee two histograms. Three similarity measuremets described below are calculated. Chi-square distace, d (H,H ) C i0 Bhattacharyya distace, d (H,H ) B (H(i) H (i)) H(i) H (i) i0 1 (8) 1 (H(i) H(i)) (9) 1 PV ( i) 359 r 0 B( i, ),for i 1,..., R (6) where B i, is the itesity at the polar coordiate i,, R is the legth of radius, ad r is the umber of o-zero value i the trasformed image B. Itersectio ratio, d (H,H ) I i0 Mi(H(i),H (i)) (10)

6 71 where H ad H deote the horizotal projectio histogram of bubble template ad potetial defect regio, respectively. The three distaces ad factor P are aggregated as a feature vector as v [ d, d, d, P] (11) C B I The SVM has bee widely used i may patter recogitio problems [9,10]. It fids a hyperplae to classify patters ito two classes. Assume that a set of vectors D belogs to two separable classes, D {( v1, d1),( v, d ),...,( v, d )}, di { 1, 1} (1) The hyperplae ca be represeted as l v vi g( v ) sg idi exp b (13) i1 where is the weight parameter, i v is support i vector, l is the umber of support vectors, σ is badwidth ad b is a bias. I the traiig phase, 60 traiig patters, icludig 30 defect patters ad 30 o-defect patters, are selected. The o-defect patters were selected from potetial defect regios. Ad the best model was selected by two-fold cross-validatio. After the traiig phase, the SVM model ca be obtaied ad processed. Figure 9 shows the structure of the SVM classifier for bubble defects classificatio. SVM 3. Experimetal Results The proposed method was implemeted by Microsoft Visual Studio 008 C# o a Itel Core i7 3.4GHz processor with 4GB RAMs. There are two LED sealig glue image databases captured from differet light sources (white ad red) with the same LEDs. Each database cotais 10 LED images icludig 75 images with bubble defects ad 45 images without bubble defect. Figure 10 shows examples of the two databases. The average ispectio time of the proposed method was 1.s per image. Three experimets were coducted i this paper. 3.1 Potetial Defect Detectio Uder Various Widow Sizes I the proposed method, potetial defect regios were detected by decisio tree. To fid the best settig for widow size, four sizes of widow (3 3, 5 5, 7 7 ad 9 9) were used for feature extractio. I this experimet, there exist 148 bubble defects i the 5 LED sealig glue images. Table 1 presets the experimetal results uder differet widow sizes. The result shows that the widow with size of 7 7 ca detect the most bubble defects. Four bubble defects were missed. These udetected bubble defects are usually blurred ad isigificat with sizes less tha pixels. Therefore, the widow with a size of 7 7 was selected i the subsequet experimets o-defect Defect Figure 9: The structure of the SVM classifier.

7 7 Specificit y t tp t Accuracy p (15) (16) (c) (d) Figure 10: Example of LED sealig glue images: ormal LED image captured from red light source; Defective LED image captured from red light source; (c) ormal LED captured from white light source, ad (d) Defective LED image captured from white light source. Table 1: Potetial defect detectio result Widow size # of potetial bubble defects # of udetected bubble defects Bubble Defect Detectio I order to evaluate the performace of the proposed method, the sesitivity, specificity ad accuracy were calculated. Assume p is the actual umber of bubble defects. The is the actual umber of ormal regios. tp is the umber of actual defective regios that the system detects. Ad the t is the umber of actual ormal regios that the system detects. The sesitivity, specificity ad accuracy are defied as follows. tp Sesitivit y (14) p 80 LED images captured from white ad red light sources were used for testig. Table shows the bubble defect detectio results uder differet lightig sources. The system detects more potetial defect regios from the LED images captured from white light source. This factor causes the experimet to have high false positives ad take loger time to detect defects. Table 3 presets the performace. These values demostrate that the proposed method ca detect bubble defects with high sesitivity. The result shows that the whole classificatio accuracy i red light is better tha that i white light. Figure 11 shows the bubble defect detectio results. Bubbles are outlied by rectagles, ad the gree rectagles outlie the real bubbles; the blue rectagles are over detected defects. These false positive regios are usually located o the edge or shadow regios, as the blue rectagles show i Figure 11. Figure 11 shows the results are more robust ad accurate with red light sources. Table : Cofusio matrix of bubble defect detectio. Predicted Actual Defect ormal Red light source Defect ormal White light source Defect 1 18 ormal Table 3: Performace of defect detectio. Light source Sesitivity Specificity Accuracy Red 90.00% 84.58% 84.7% White 87.14% 73.34% 73.55%

8 Compared with other methods I the image, the bubble ca extract shape features for idetificatio. Sice images use differet segmetatio methods to segmet the bubble, the results of segmetatio image are ot the same. That could affect the efficiecy of the idetificatio results. This experimet used differet segmetatio algorithms to compare defect ispectio. The popular edge detectors icludig Sobel ad Cay edge detectors ad Otsu s method were selected for compariso with the proposed approach. Table 4 shows the performace compariso of four segmetatio methods. There are 140 bubble defects ad 4630 ormal regios which are the potetial defect regios i 40 LED sealig glue images. I the proposed method, the sesitivity ad specificity is 0.9 ad 0.85, respectively. Although Cay s method has a higher accuracy, its sesitivity is less tha the proposed method, which meas Cay s method caot detect bubble defects accurately. As show i Table 4, these values demostrate that the effectiveess of the proposed method ca efficietly ispect the bubble defects i the LED sealig glue images. Figure 11: the detected bubble defects from the white light source, ad the detected bubble defects from the red light source. Table 4: Performace compariso of the four segmetatio methods. Propose Cay Sobel Otsu d Defect regio Defect ormal ormal regio Defect ormal Sesitivity Specificity Accuracy Coclusios LED ca be produced quickly with automatic maufacturig systems. Automatic visual defect ispectio plays a importat role with the beefits of low-cost ad high accuracy. This paper proposed a bubble defects detectio scheme for LED sealig glue images. The silicoe regio was extracted so that the regio eeded to be ispected was reduced, ad thus the defect regios were determied. The positios of potetial bubble defects ca be obtaied by usig decisio tree. The SVM is applied to classify the potetial defects ito defect ad o-defect. The experimetal data show that LED sealig glue image i red light source has higher accuracy tha i white source. The results show that the proposed method is effective to detect bubble defects o LED sealig glue images.

9 74 Ackowledgmets This work was supported by the atioal Sciece Coucil Taiwa, R.O.C, uder the grats SC E Refereces [1]. W.-C. Li, D.-M. Tsai, Wavelet-based defect detectio i solar wafer images with ihomogeeous texture, Patter Recogitio, vol. 45, pp , 011. []. H. Liu, W. Zhou, Q. Kuag, L. Cao, B. Gao, Defect detectio of IC wafer based o two-dimesio wavelet trasform, Microelectroics Joural, vol. 41, pp , 010. [3]. C.-H. Li, C.-Y. Chag, M.-D. Jeg, Applyig regioal level-set formulatio to post-sawig four-elemet LED wafer ispectio, IEEE Trasactios o Systems, Ma, ad Cyberetics--Part C: Applicatios ad Reviews, vol. 41, o. 6, pp , ov [4]. Y.-C. Chiou, Itelliget segmetatio method for real-time defect ispectio system, Computers i Idustry, vol. 61, o. 7, pp , 010. [5].. Otsu, A threshold selectio method from gray-level histograms, IEEE Trasactios o Systems, Ma ad Cyberetics, vol. 9, o. 1, pp. 6 66, Ja [6]. D. Bradley ad G. Roth, Adaptive thresholdig usig the itegral image, Joural of Graphics Tools, vol. 1, o., pp. 13-1, 007. [7]. Quila, J. R., C4.5: Programs for Machie Learig, Morga Kaufma Publishers, [8]. C. Yag, R. Lu,. Che, A fast method for large aperture optical elemets surface defects detectio, Advaces i MSEC, Vol., pp , 011. [9]. C. C. Chag ad C. J. Li, LIBSVM: a library for support vector machies, 001. Available: [10]. C. Cortes ad V. Vapik, Support-vector etwork, Machie Learig, Vol. 0, pp , Chua-Yu Chag received the Ph.D. degree i electrical egieerig from atioal Cheg Kug Uiversity, Taia, Taiwa, i 000. He is curretly a Distiguished Professor ad Dea of Research & Developmet, atioal Yuli Uiversity of Sciece ad Techology, Taiwa. He is the chair of IEEE Sigal Processig Society Taia Chapter, ad a Associate Editor of the Iteratioal Joural of Cotrol Theory ad Applicatios. His curret research iterests iclude eural etworks ad their applicatio to medical image processig, wafer defect ispectio, digital watermarkig, ad patter recogitio. I the above areas, he has more tha 150 publicatios i jourals ad coferece proceedigs. He was a member of the orgaizatio ad program committees for more tha 50 times, orgaized ad chaired for more tha 30 techical sessios for may iteratioal cofereces. He is a Life Member of IPPR ad the Taiwaese Associatio for Artificial Itelligece (TAAI), seior Member of IEEE, ad is listed i Who s Who i the World, Who s Who i Sciece ad Egieerig, Who s Who i Asia, ad Who s Who of Emergig Leaders. Yi-Feg Li received the B.S. ad M.S. degree i 009 ad 01, respectively, from the Departmet of Computer Sciece ad Iformatio Egieerig, atioal Yuli Uiversity, Yuli, Taiwa. His research iterests iclude eural etworks, digital image processig ad their applicatios.