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Volume-4, Issue-4, August-2014, ISSN No.: 2250-0758

International Journal of Engineering and Management Research Available at: www.ijemr.net

Page Number: 143-146

Detection of Defects in Printed Circuit Boards using Fuzzy Logic and

Correlation Coefficient Neha Koul

1, Dr. Gurmeet Kaur

2, Beant Kaur

3

1M.Tech student in Department of Electronics and Communication Engineering, Punjabi University, Patiala, INDIA

2Professor in Department of Electronics and Communication Engineering, Punjabi University, Patiala, INDIA

3Assistant Professor in Department of Electronics and Communication Engineering, Punjabi University, Patiala, INDIA

ABSTRACT

A Printed Circuit Board (PCB) is a card made

specifically for attaching electronic components on it.

Inspection of PCB is necessary in order to reduce the defects

otherwise it can lead to complete circuit failures. In this paper

we have presented a PCB Inspection System which involves

the use of correlation coefficient and fuzzy logic in order to

detect the defects and the inspection system is applied at the

time of manufacturing, i.e., the making of bare PCB. The

inspection system also gives the degree of defectiveness

present in the PCB. Typical defects that can be detected are

over etchings (opens), under-etchings (shorts), holes etc.

Keywords- correlation coefficient, fuzzy logic, fuzzy

image processing, membership functions.

I. INTRODUCTION

During the manufacturing of PCB there are some

defects commonly found on PCB. These defects are

broadly divided into two categories, potential and fatal

defects. Short-circuit and open-circuit defects are examples

of fatal defects category. Breakout, under etch, missing

hole, and wrong size hole fall in potential defects category.

Fatal defects are those defects in which the PCB does not

meet the objective for which it is designed, while the

potential defects are those which compromise the PCB

performance during utilization [1].

The detection of these defects at an early stage in the

production process is beneficial and avoids multiplication

of cost due to delayed detection of defects. Thus, it is

important to work out a reliable method to detect the

defects in the PCBs. There are many types of defects

which plague printed circuit boards (PCBs). The reason for

this is the immense complexity and miniaturization of

chips which are mass produced in the millions [2]. Each

chip contains thousands of individual systems all working

harmoniously to produce a certain output. Some defects are

caused by impure materials. Another defect is where there

are physical problems with the material [6]. Voids,

fractures, and de-lamination can all combine to reduce or

corrupt PCB performance .In this study correlation coefficient

is used to find whether the PCB is defective or not and if PCB is

found to be defective then its degree of defectiveness is calculated

using correlation coefficient.

II. METHODOLOGY

For the inspection of the printed circuit

boards we first need to have a reference image of a

PCB which is absolutely defect free i.e. defect less.

Then the correlation of the PCB image which is to be

inspected is found with respect to the reference

image. If correlation is found to be zero then PCB is

not defective otherwise PCB is defective.

Figure 1: Reference image

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Figure 2: Test Image

Fig. 1 and 2 show the examples of reference

PCB image and test PCB image. The defective image

has a missing joint which is now to be detected [5].

A. Inspection Flow Chart

In our inspection system, we will first find

correlation between the image which is to be

inspected with the reference image. Then the value of

the correlation coefficient will be given to the fuzzy

system whose output will tell the degree of

defectiveness in the PCB.

Fig 3: Inspection Flow Chart

B. Correlation Coefficient Correlation is a measure of the strength and

direction of the linear relationship between two

variables that is defined as the (sample) covariance of

the variables divided by the product of their (sample)

standard deviations. The next step in the inspection

system will be the calculation of the correlation

coefficient. The value of the correlation coefficient

will give the amount of similarity between the

inspected image and the standard image [3]. This

value of correlation will be given input to the fuzzy

system on the basis of which we will calculate the

degree of extent of defectiveness. In the case of

above test PCB image correlation was found to be

0.96. This value is then fed to the fuzzy tool box

which calculates the degree of defectiveness present

in the PCB.

C. Design And Development Of Fuzzy Expert

System

Fuzzy comprises the process of transforming

crisp values into grades of membership functions

for linguistic terms of fuzzy sets. Steps in fuzzy logic

are fuzzification, evaluation of rules and finally

defuzzification. To design the system, the FIS tool in

MATLAB R2013a is used [4].

Fuzzy Image Processing is the collection of

all approaches that understand, represent and process

images, their segments and features as fuzzy sets

.The representation and processing depend on the

selected fuzzy technique and on the problem to be

solved [7]. First, the linguistic values and

corresponding membership functions of input and

output have been determined. Samples of values and

corresponding membership functions for the

correlation coefficient are shown in Figure 4. Fig 5

shows the membership function and linguistic

variables for the output PCB defect.

Fig 4: Linguistic variable and membership function

of Correlation Coefficient

Select the reference image

Calculation of correlation of

correlation coefficient

Fuzzy Reasoning

Buffer the image to be

inspected

Result: Degree of

defectiveness

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Fig 5: Linguistic variable and membership function

of PCB defect

The membership function for correlation coefficient

is low, medium and high. All are represented in

Gaussian waveforms. The range of values for low is taken

from 0 to 0.3, for medium it is taken from 0.4 to 0.7 and

for high it has sample values from 0.7 to 1.Then the

output variable and its corresponding membership

functions have been determined. Samples of values and

corresponding membership functions for output are

shown in Figure 5 above. The membership function for

output is low, medium and high [8]. All are represented in

Gaussian waveforms. The range of values for low is taken

from 0 to 0.3, for medium it is taken from 0.4 to 0.7 and

for high it has sample values from 0.7 to 1. Gaussian

waveforms are generally used in fuzzy because the input

is not exact so the range of input can be easily shown

using Gaussian waveform [9].

D. MATLAB 2013

Correlation coefficient is first calculated of the test

image with respect to the reference image and then this

value is fed to the fuzzy system whose output will give

the extent of defectiveness present in the PCB based on

the fuzzy rule base.

III. RESULTS

Fuzzy expert system is used to determine degree of

defectiveness present in the PCB. This design consists of

1input and 1 output. The inputs consist of correlation

coefficient while the output is the extent of defectiveness

present in the PCB. The variables are used like low,

medium and high for input and low, medium and high for

output. The outline of our proposed fuzzy expert system

can be shown in Fig.6. Mamdani method is used for

fuzzification.

Fig 6: Fuzzy Expert System

Rule base is shown in figure 7. Three rules

are used in this system. The rules have been

developed using if-then method. The rules have been

made on the basis of the FAM table given below.

Table 1 : Fuzzy Associative Memory Table

Correlation Defect

Low Highly defective

Medium Medium defective

High Less defective

One Not defective

Fig 7: Rule Base

Using these rules, the result risk in term of

percentage (%) has been computed. Figure 8 shows

the ruler view of the graph obtained between

defectiveness of the PCB against correlation

coefficient. Surface view of the resultant graph is

shown in figure 9.

Fig 8: The ruler view of result rules of fuzzy expert

system

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Fig 9: Surface view of fuzzy expert system

It is clear from the graphs that as the correlation

coefficient increase the defect in Printed Circuit Board

decreases. As soon as the correlation coefficient becomes

zero the defect also reduces to zero. The PCB above was

found to be 20% defective.

IV. CONCLUSION

The PCB is analyzed and the defects of PCB are

calculated. Due to the use of correlation coefficient the

accuracy of the system is very high. By the use of the

above inspection method we come to know that whether

the PCB is defective or not and if the PCB is found to be

defective its degree of defectiveness is calculated.

V. FUTURE WORK

The proposed method can also be extended to

detection of defects in fabric; wood etc. The reference

image and the image to be tested should have same

alignment. So this alignment problem also has to be

worked upon

REFERENCES

[1] M. Moganti, F. Ercal, C. H. Dagli and S. Tsunekawa,

“Automatic PCB Inspection Algorithms: A Survey”,

Computer Vision and Image Understanding journal,

vol.63,1996. [2] J. F Canny, “A computational approach to edge

detection”, IEEE Transaction on Pattern Analysis and

Machine Intelligence, vol. 8, pp 679-698, 1997. [3] W.Y. Wu, M.J. Wang and C.M. Liu, “Automated

Inspection of Printed Circuit Boards through Machine

Vision”, Computers in Industry, vol.28, no.2, pp.103-

111,1998. [4] Jan Hanca, Adrian Munteanu and Peter Schelkens,

“Segmentation-based Intra Coding Of Depth Maps Using

Texture Information”, proceedings of IEEE conference on

Compression of depth information for 3D rendering , 2013.

[5] G. Rafiee, S.S. Dlay, and W.L. Woo,

“Unsupervised Segmentation of Focused Regions in

Images with Low Depth of Field”, International

Journal of Computer Vision, vol. 70, pp. 109-131,

2013. [6] I. Ibrahim, S.A.R Abu-Bakar, M.M. Mokji et al.

“Performance measurement of thresholding

algorithms in printed circuit board inspection

system”, proceedings of 3rd International Conference

on Computational Intelligence, Modeling and

Simulation,2012.

[7] I. Ibrahim, S. Rahman and Syed, “A Printed

Circuit Board Inspection System with Defect

Classification Capability”, International Journal of

Innovative Management, Information & Production,

Vol. 3, March 2012.

[8] D. Perng, C.Liu, Y. Chen, and C. Chou,

“Advanced PCB Vision Inspection Machine

Development”, Department of Industrial Engineering

and Management National Chiao-Tung University,

15th

IPPR Conference on Computer Vision, Graphics

and Image Processing, 2002. [9] Z.Ye , Prasad, S.Wei Li , “Classification Based

on 3-D DWT and Decision Fusion for Hyperspectral

Image Analysis Geoscience and Remote Sensing

Letters”, proceedings of IEEE conference , 2014.

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