Vision for Solder Paste Depositing

2007 IEEE International Conference on Control and Automation WeC5-3 Guangzhou, CHINA-May 30 to June 1, 2007

An

Integrated Inspection Method based

on

Machine

Vision for Solder Paste

Depositing

ShenglinLu

theCollege of Mechanical Engineering South ChinaUniversity of Technology

Guangzhou, China joolu@163.com

XianminZhang

theCollege of Mechanical Engineering South ChinaUniversity of Technology

Guangzhou, China zhangxm@scut.edu.cn

Yongcong Kuang theCollege of Mechanical Engineering

South ChinaUniversity of Technology Guangzhou, China

Abstract-The solder paste depositing inspection is very

importantin the process of SurfaceMounting for print circuit

board (PCB). Anintegrated inspection approach based on the

machine vision was presented in the study. The method

developed in thispaper can identify the major defects of the solder pastedepositing,such asdisplacement, deficiency,excess,

bridgeandoverflow.Firstly, position compensation is appliedto

improve the inspection accuracy. Secondly, an image

enhancementalgorithmbased on the textureisdeveloped, which

canenlargethedifferences ofthegrayscalevalues between the

PCBimagesand the solder pasteimages. Thirdly,theimages of

the printed PCB are analyzed by using the particle analysis

method and the two-dimensional (2D) inspection results are

obtained. Apseudo three-dimensional (3D) inspection approach is further proposedtoidentifythedefectsin the3Dstate.Finally, experimentresults arepresented,which illustratethevalidity of

theapproach.

Keywords- machine vision; inspection; solder paste depositing

I. INTRODUCTION

Thescreenprinter isoneof thekey equipments of theSMT industry [1]. Its performances directly affect the subsequent handling and the product quality. Thus, it is necessary to

inspect the Printing Circuit Board (PCB) afterscreenprinting. It was reported that 50-70% PCB assembling defects were

from the stage of solder pastedepositing [2]. The inspection of the solder pastedepositingisoneof themostimportantstages inthe process control of SMTproduction line [3]. The defects of solder paste depositing include displacement, deficiency, excess, bridge, overflow, and so on. The typical inspection methods for the solder paste depositing include manual inspection, the laser scan inspection [4] and the inspection based on machine vision [5]. The manual inspection is a

popular method because of its simple operation; however, it has many serious disadvantages, such aslow efficiency and high probability of miss judgment. The laser scan approach provides more detailed defect information because of its 3D inspection function. However, the 3D model of the solder paste is acquired by point or line scans of a laser [6], so the inspection very slowly. Furthermore,the laser scan device is

expensive. The method basedonmachine vision has the merits ofhigh inspection accuracy and speed, thus itcanbe used in PCB solder paste depositing inspection of the screen printer. Moreover, no extra hardware cost is needed, because the approach canmake full useof theexisting hardwareresource

of the vision system of thescreenprinter.

The main procedures of solder paste inspection basedon

machine vision includeimage acquisition, image enhancement and defectanalysis.Inordertoimprove the performance of this inspection approach, the position compensation method is utilized to determine precise position before acquiring PCB images. An image enhancement method basedon texture is developed d, whichcanenlarge the differences of the grayscale values between PCB baseimages and solder paste images.In

addition, the printed PCB imagesareanalyzed by using the 2D inspection algorithm basedontheparticle analysis method. A pseudo 3D inspection approach is appliedtoinspect the defects of solder pastedepositing involume. Finally,theexperiment results illustrate thevalidity of the proposed method.

II. THESCREEN PRINTER WORKFLOW

As one of thekey equipments, the main function of the solder pastescreenprinteristodepositthe solder pasteonPCB precisely. The screen printer is composed of conveyer, squeegee system,machine vision system andplanar3degrees of freedom (DOF) adjusting unit, etc. The machine vision systemis usedtoalignthe PCB and thestencil,andtoinspect solder pastedepositing. Acycleof work flow of thescreenis shown inFig.1. Thepositionand pose of the PCB isnotfixed afterbeingloadedbythe conveyer.Thus, the machine vision system need to measure the marks positioning by pattern matching so that the printer can find the differences of the position and pose of the PCB and the stencil. Then, these differences can be compensated byuse of theplanar 3-DOF adjustingunit.

Manuscript received November1,2006. This workwassupported bythe

Teachingand Research Award Program forOutstandingYoungTeacher in

HigherEducation Institutions ofMOE, P.R.C., theGuangdongHong Kong

TechnologyCooperationFunding(Dongguan Project20061682)and the ResearchProject of Ministry of Education andGuangdongProvince

(2006D90304001). ShenglinLuiswith the South ChinaUniversityof

V~~~

+

y

x

Figure 2. Theposition compensationofthePCB

IV. IMAGEENHENCEMENT

Comparedwith the solder pasteand the base of thePCB, thegrayscalesvalue of thepadis muchhigher.The difference

can bedistinguished easily by using athreshold function. But

thegrayscales of solderpaste and the base of PCB arevery

similar,and theirhistograms are evenpartially overlapped.To

accurately distinguishtheimageof solderpasteand the base of PCB, an algorithm based on texture was presented in this

section.

Figure1. The Workflow of the Screen Printer.

III. POSITION COMPENSATION

As shown in Fig.1, the inspection program needs to be configured prior to inspection. The inspection program configurations include the setting of inspection sites, threshold values, image enhancement parameters, and so on. The inspection performance willbe affected by the differences of theposition and the pose between the PCB for configuration and theinspectingone. Here,aposition compensation method isusedtocompensatethe differences of the position and pose comparing with the PCB priortotheimages acquisition. These differences areapproximately equal to coordinates difference of thetwomarks of thePCBs,asshowninFig. 2. The 6 ands canbe calculatedby the following equation:

Ml'=Tm1+s

M21=,Tm2+s (1)

wheremland m2represent the coordinates of markl and mark2 in the inspection program configuration, respectively. mlI' and M2' are the coordinates of markl and mark2 in inspection, respectively. sisthe shiftvectorand

s=(Alx, Jly)T.

Tisthe rotation matrix whichcanbeexpressedas: cos6 -sin6

T=

sin6 cos )

Fromequation (1), one canfind that thereare 3 unknown variables and 4 equations. The 0can be calculate easily by using the trigonometric function, then, the over constrained problem canbe solved by using the least square method. The applying of the method of position compensation method can improve the inspection accuracy

Figure 3. Image ofsolder paste. The (a) is the solder paste imageby magnified4times,pitch=0.3mm, and the (b) is the solder paste image acquire

by the vision system, pitch=l.Omm.

AsshowninFig.3 (a),the solder paste is mainlycomposed of little balls and itsimage has pittexture.However, theimage of the PCB base and pad is very smooth. According tothis feature, the images can be enhancedby increasing the solder pastegrayscale value.

Itis well knownthat thetextureof the imagecanevaluated byann-order statisticalmomentof thehistogram whichcanbe expressedas[7]

L-1

Aln

y

(Zi _-m)nP(Zi)

i=O

where ziis a random variableindicating intensity,p(zi) is the histogram of the intensity levels in a region, L is the number ofpossible intensity levels, and misthemeanvalueof grayscale.

However,thehistogram doesnotexternalizetheposition of pixel information.Thus, the images needtobeseparated intox Xy parts before using n-order statistical moment,

kin

(X, y) iS the n-older value of rowx, column y partof theimage. The enhancementalgorithmbaseonn-order statisticalmoment can be writtenas

{

g(i,j)

f

(i, j)

+h

.u,,

(x, y)

u, >h

g(i, j) f

(i,

j)

u, <h

whereJ(i,j) istheimagewithout enhancement,g(i, j) is the imagewith enhancement, and h is a weight.

This algorithm can enhance the images by increasing the grayscale of the solder paste part, so the solder paste and the base ofPCB canbe distinguished easily. Theenhanced images can be separated into pad particle, solder paste particle and dark loop around solder paste particle by using a multi-thresholds function.

V. INSPECTION METHOD

A. 2D InspectionBased on Particle Analyze method The Particle analysis method has many advantages, such as simpleandhigh speed. Furthermore, many particle factors are shift, scaling androtation invariant. Thus, the particle analysis approachissuitable for2Dinspection.

The defects of solder paste depositing can be inspected by analyzingthe position, area andshapeof theparticlesof solder paste and pads, which can be expressed by the following factors[8],

Center: centerposition of theparticle. A: areaof the particle.

Ah: area of the hole in the particle.

ACH

Areaof the

particle

ConvexHull. P:Length of theouterboundary of the particle.

Hydraulic Radius: The particleareadividedby the particle perimeter.

Heywood Circularity Factor: HC=P/(2sqrt( 7T A)), Perimeter divided by the circumference of a circle with the

same area. The closer theshape of the particleis to adisk,the closer theHeywood circularity factoristo1.

I,,:

momentofinertiawithrespecttothexaxis.

Iyy

momentofinertiawithrespect totheyaxis.

Type Factor: TF=A2I(4ff.sqrt(I

I,)),

thetypeof the

particle.

Packing Ratio: PR=AI(A+

Ah)

it is used to judge whetherdeficiency of solderpaste existor not.

The values of the factor mentioned above areinputtedto the defectanalyzer after being calculated. The defect analyzer is used to determine the defect existing or not by using a synthesis weight method. The defect analyzercanfindoutthe defects of the solderpaste, such as displacement,

deficiency,

excess,bridge and overflow. Though the2Dinspection method

can determinemost of the solderpaste depositing defects, it

can notinspect the volume defects of solderpastedeposition. B. Pseudo-3D inspection

Note that the 3D inspection algorithm can find out the defects of paste dose in volume, however, it needs a laser

scanner and it is relatively slowly. To overcome these disadvantages, a pseudo-3D inspection approachwas usedin

order tocalculate the volume ratio by creating a simple 3D model of the solder paste depositing.

As shown in Fig. 3 (b), there is a dark loop around the solderpaste. One reason for the darkloop is that the PCB is illuminated by the coaxial illuminator of the vision system, thus the ray is perpendicular to the PCB according to the principle of reflection. If only the ray is perpendicular to the surface, the reflected ray is captured by the camera, so the image isbright; otherwise the image is dark, as shown in Fig. 4(a). ray

FaSolder

Paste Base of PCB (a) Vu)

Figure4. 3DModelofSolderpaste.The(a) is thesideviewandthe(b)is the top view.

AsshowninFig.4(b),

Ap

istheareaof particle of the pad, A,istheareaofparticle of the solderpasteandAd istheareaof particle of the dark loop around the solderpaste.The volume ratioof the solderpastedepositcanbe obtained by

VaVVt =1

l2(A,

+

(A,

+

Ad)

h

/(AP

.h)

=

(I/

2Ad

+

A,)

I

AP

(2)

whereVa represents the actual volume of the solder paste and

V,

isthe theoretical volume of the solderpaste.

The defectscan be foundout byathreshold accordingto the volume ratio. This pseudo-3D inspection method can be carriedoutfastcomparewith the laserscannerinspection.

VI. EXPERIMENTAND ANALYSIS

Inordertoverify the performance of the integrated method,

someexperiments have been carriedout. The machinevision systemwasshown inFig.5, thevision systemincludesa NM-PC1I409 image acquirecard, two Teli-Cs8630i cameras, two AllyVision-COR30 coaxial illuminators. The solder paste is lead free.

Fig. 7 shows the misalarm ratesof solder paste depositing defects for four kinds of electronic component From this figure, one finds that the misalarm rates reduced by using position compensation and texture-based image enhancement method.

Misalarm Rate

8

Figure 5. The hardware of the visionsystem.

The histogram of the images of the solder paste and PCB base is showninFig. 6. Fig. 6 (a) is thehistogram of the image of the PCB base. Fig. 6(b) is the histogram of the image of the solder paste before image enhancement. Fig. 6(c) is the histogram of image of solder paste after image enhancement. It canbeseenthat thehistogram value of solder paste imagewas increased after image enhancement, and the difference of the mean grayscale valuesbetween the two images varies from 4.6 (109.8-105.2) toI3.7 (118.9-105.2). The increase value was related to theweight factor h.

(a) Histogram of the image of the PCB base

ChiplO05 ChipO4O2 QPF128 BGA128 P=0.5 P=l.0

*WithoutPosition Compensation & Image

Enhancement UWith Position Compensation CWithImage Enhancement EWith Position Compensation & Image

Enhancement

Figure7. Misalarmratescomparison.

TABLE I MISALARM RATES

Defecttypeofsolderpaste Chip Chip IC IC

1005 0402 QFP BGA Displacement 3 9 5 7 Deficiency 1 6 7 5 Excess 2 3 2 2 Bridge, 0 0 1 0 Overflow 0 1 0 2

TableIshows the misalarmratesof five defects of solder paste depositing for four electronic components, two kinds chip andtwokinds of ICs. Onecan finds that the misalarm rateof thedisplacement defect is thehighest and is relatedto threshold valuesetbyuser. Whilst, the defect misalarmrateof the small and roundpads ishigher.

The results ofpseudo-3D inspection are shown in Fig. 8. Using pseudo-3D inspection algorithm measure thechiplO05 and chip 0402 fifteen times. The results show that the fluctuation range of the volume ratio describedin equation (2) is from 50to 110.Therefore, itcanbe concluded that the 3D inspectionmethod is stable.

120

(b)Histogram of the image of the solderpastebeforeimage enhancement

100

> 80

60

40

(C)histogram of image of solderpasteafterimage enhancement 0 2 4 6 8

Times 10 12 14 16

Figure 6. Histogram of the image of the solderpasteand PCB Figure 8. Pseudo-3D Inspection Results

-u-Chip1005

VII. CONCLUSION

An integrated inspectionmethod of solder paste depositing based on the machine vision was presented. Toimprove the positioning precision of the inspection locations, the position compensation method was performed. A texture-based algorithm is also proposed to enlarge the differences of the grayscale values between the solder paste and the PCB base, which makes the multi-threshold method more stable and precise. Furthermore, the 2D and pseudo-3D inspection approach can be used to inspect the solder paste depositing defects effectively. The experiment results demonstrated the feasibilityand robustness of theintegratedmethod.

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