automatic pcb defect detection using digital image processing techniques

AUTOMATIC PCB DEFECT DETECTION USING IMAGE SUBTRACTION METHOD

Pawan Nayanajith

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INTRODUCTION• A Printed Circuit Board (PCB) is everywhere from a small toy to a big electronic

machine which we used in our daily life.• PCB consists of circuit with various electronic components mounted on surface. • During the manufacturing of PCB many defects are introduced which are harmful

to precision circuit performance. • To reduce manufacturing costs associated with defected bare PCBs, the inspection

of bare PCBs is required as the foremost step of the manufacturing process.• The objective of printed circuit board (PCB) inspection is to verify that the

characteristics of board manufacturing are in conformity with the design specifications.

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PCB DEFECTS• PCB defects can be categorized into two groups …• Functional defects - Functional defects can seriously affect the

performance of the PCB or cause it to fail.• Cosmetic defects - Cosmetic defects affect the appearance of the

PCB, but can also threaten its performance in the long run due to abnormal heat dissipation and distribution of current.

Figure : Template PCB

Figure : Defected PCB

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• There are 14 known types of defects for single layer, bare PCBs as shown in below.

Fig 2: Defective image of bare PCB

1. Breakout 2. Pin hole 3. Open circuit 4. Under-etch 5. Mouse bite 6. Missing conductor 7. Spur 8. Short 9. Wrong size hole 10.Conductor too close 11.Spurious-cooper 12.Excessive short 13.Missing hole 14.Over etch

Figure 1: Template image of bare PCB

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CLASSIFICATION OF DETECTION TECHNIQUES

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TECHNOLOGY DESCRIPTION• An arithmetic or logic operation between images is a pixel-by-pixel

transformation. • It produces an image in which each pixel derives its value from the value of pixels

with the same coordinates in other images.• If A and B are the images with a resolution XY, and Op is the operator, then the

image N resulting from the combination of A and B through the operator Op is such that each pixel P of the resulting image N is assigned the value

pn = (pa)(Op)(pb) ; where pa is the value of pixel P in image A, and pb is the value of pixel P in image B.

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IMPLEMENTATION OF METHOD• Inspection flow chart• The PCB inspection using Image subtraction method is performed in

steps. As shown in flow chart below, STEP 01

• Load the reference and test images to the computer

STEP 02

• Convert the images to grayscale image

STEP 03

• Threshold the image

STEP 04

• Convert to binary image

STEP 05

• XOR operation on the images

STEP 06

• Resultant image where defect detected

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1. Loading the Images• The image of the Printed Circuit Board with no defects is

loaded on to the computer.• The Board which needs to be inspected is placed on the

glass platform of suitable size. • A camera is used in order to take the picture of the PCB. • The image captured is loaded on to the computer. • The image captured will be considered for the further steps.

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2. Converting to gray scale• Instead of giving equal contributions to all the colors, in case of this

context we have to decrease the contribution of red color, and increase the contribution of the green color and put blue color contribution in between these two.

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3. Thresholding• It is useful to be able to separate out the regions of the image corresponding to the

objects in which we are interested, from the regions of the image that correspond to background.

• Thresholding often provides an easy and convenient way to perform this segmentation on the basis of the different intensities or colors in the background or foreground regions of an image.

• Each pixel in the image is compared with threshold. If the pixels intensity is higher than the threshold, the pixel is said to, say white (binary 1) in the output. If it is less than the threshold it is said to be black (binary 0).

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4. XOR Operation• The overview of the XOR/Subtraction operation process is shown below

• To perform the image subtraction operation, it is required that both images have same size in terms of pixels. The logical OR operation will show us the defect in inspected image as compared with reference image.

FIG. RESULTANT IMAGE

Pixel(image 1)

Pixel(image 2)

Pixel(Output Image)

0 0 00 1 11 0 11 1 0Truth Table For XOR Operation

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5. Resultant Image• The resultant image obtained after comparing the image of the PCB with

the master PCB image if defects are found,

FIG. COMPARING REFERENCE AND TEST IMAGE (DEFECTS FOUND)

FIG.5 RESULTANT IMAGE(DEFECTS NOT FOUND)

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CONCLUSION AND DISCUSSION• It is very important and essential to examine Printed Circuit Boards for defects as they are

importantly useful. • Using Image processing techniques and algorithms, examination process has become fast,

reliable and effective compared to manual process as it excludes labor intensive job. • Automated nature of the examination process makes it more advantageous than manual

one.

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REFERENCES• Xiong Zhenjiao. Research on the Detection Method Based on Image for

Defect in Circuit Board. [Mj. School of Information Engineering Nanchang Hangkong University, 2012.• N.G.Shankar, Z.W. Zhong. Defect detection on semiconductor

wafersurfaces[I].Microelectronic Engineering , 2005: 337-346. 159.• JIANG Ke-wan, WU Qi. Application of Image Recognition Technology in PCB

Precision• T. Taniguchi, D. Kacprzak, S. Yamada, M. Iwahara, and T. Moyagashi,

‟Defect Detection on Printed Circuit Board Using Eddy-Current Technique and Image Processing‟, IOI Press, 2000..

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