A Novel Defect Inspection Method for the TFT-LCD Image Based on ...

A <strong>Novel</strong> <strong>Defect</strong> <strong>Inspection</strong> <strong>Method</strong> <strong>for</strong> <strong>the</strong> <strong>TFT</strong>-<strong>LCD</strong>
<strong>Image</strong> <strong>Based</strong> on Human Visual System
Tae-Eun Song 1 , Se-Yun Kim 2 , Jong-Hwan Kim 3 , and Kil-Houm Park 4
1 Department Name, University Name, City, State, Country
2 Department Name, Company / Institution Name, City, State, Country
Abstract - The <strong>TFT</strong>-<strong>LCD</strong> image has non-uni<strong>for</strong>m
brightness that is <strong>the</strong> major difficulty of finding <strong>the</strong> defect
region called Mura. In order to facilitate Mura detection,
globally large-varying background signal has to be leveled
and also Mura signal has to be enlarged. In this paper,
Mura signal magnification and background signal
flattening methods by <strong>the</strong> use of adapted contrast sensitivity
function(ACSF) in DCT domain and polynomial
regression(PR) are proposed. In <strong>the</strong> enhanced image, Trimodal
thresholding segmentation technique is used <strong>for</strong>
finding both Dark and White Mura at once. To leave out
reliable defect, false region elimination algorithms based
on HVS (Human Visual System) are also proposed. The
experimental results prove that <strong>the</strong> proposed algorithm is
novel enhancement method and it can be applicable to real
automated inspection system.
Keywords: <strong>TFT</strong>-<strong>LCD</strong> (Thin Film Transistor-Liquid Crystal
Display), Polynomial Regression, Discrete Cosine
Trans<strong>for</strong>m, Human Visual System, Contrast Sensitivity
Function.
1 Introduction
Recently, <strong>the</strong> thin film transistor liquid crystal displays
(<strong>TFT</strong>-<strong>LCD</strong>s) has become <strong>the</strong> major flat panel displays
(FPDs) <strong>for</strong> <strong>the</strong>ir volume and versatile usage [1]. The more
<strong>the</strong> <strong>TFT</strong>-<strong>LCD</strong> market grows <strong>the</strong> more need <strong>for</strong> automated
inspection systems exist. However most of <strong>the</strong> final
inspections of <strong>LCD</strong> panels have been done by human
observers. Human vision-based inspection [2] systems have
some disadvantages that each observer tends to have
subjective defect decision levels which can vary according
to time and physical conditions. There<strong>for</strong>e, in order to
increase <strong>the</strong> productivity and reliability of products
automated inspection systems are highly required.
The <strong>TFT</strong>-<strong>LCD</strong> panel images have non-uni<strong>for</strong>m illumination
variations due to imperfection of light source and unequal
liquid crystal distribution and so on. The <strong>TFT</strong>-<strong>LCD</strong> panels
do not have a light source, <strong>the</strong>y need an external light source
called back light unit (BLU). Despite every ef<strong>for</strong>t to make
BLU as an ideal plane light source, BLU still allow
illumination variations measured by luminance uni<strong>for</strong>mity
(LU) as more than 80% [3]. The LU is expressed by <strong>the</strong>
following equation:
Luminance
L min
Uni<strong>for</strong>mity (LU) 100
(1)
L
where L and max L are <strong>the</strong> maximum and minimum
min
luminance values under <strong>the</strong> predefined positions of a <strong>TFT</strong>-
<strong>LCD</strong> panel.
Generally, <strong>TFT</strong>-<strong>LCD</strong> signal is composed of slowly varying
background signal, noise signal and defect called as Mura
signal as shown in Fig.1. The background signal and noise
signal make it difficult to find Muras in <strong>the</strong> observed signal
(Seen). So, in order to help detecting Muras, we should
flatten background signal, reduce noise and enlarge Mura
signal.
max
Fig. 1. The line profiles of <strong>TFT</strong>-<strong>LCD</strong> image
The most effective and useful approach to facilitate Mura
segmentation is background signal flattening by using
polynomial approximation (PA)[4] or B-spline[5]. PA or Bspline
seeks <strong>for</strong> <strong>the</strong> best fitting surface or plane which best
describes <strong>the</strong> shape of <strong>the</strong> signal in <strong>the</strong> sense of minimizing
mean square error. But PA or B-spline has a tendency to
reduce somewhat of <strong>the</strong> difference between <strong>the</strong> background
signal and Mura signal. That makes it difficult to find Muras
as <strong>the</strong> Muras get fainter.
Ano<strong>the</strong>r approach to easy Mura segmentation is random
noise elimination. Denoising method such as various

Wavelet Trans<strong>for</strong>m domain research has <strong>the</strong> best results in
case of precise noise modeling [6][7]. There is a problem in
<strong>the</strong> random noise modeling because <strong>the</strong> random noise is
expressed by camera noise, camera distortion and random
noise, etc.
And <strong>the</strong> o<strong>the</strong>r method to help Mura segmentation is <strong>the</strong>
enlargement of Mura signal. If <strong>the</strong> frequency band of Mura
signal could be found exactly, we could emphasize it by
magnifying this band. Generally, because <strong>the</strong> signal affects
all frequency spectrums, finding precise cut-off frequency of
<strong>the</strong> Mura signal is impossible. But from <strong>the</strong> human visual
system point of view, we can emphasize more visible
frequency given by <strong>the</strong> CSF experiment [8-10]. One of
major HVS characteristics is that <strong>the</strong> HVS has different
frequency sensitivity. Adapted CSF is proposed to visualize
more salient object such as Muras.
In this paper, <strong>the</strong> <strong>TFT</strong>-<strong>LCD</strong> image defect detection
algorithm based on HVS is proposed. The PR is used <strong>for</strong>
background signal flattening while <strong>the</strong> proposed ACSF are
used to enlarge Mura signal. Then tri-modal thresholding,
which determines defect candidate as out of mean value, is
employed <strong>for</strong> Mura segmentation. Finally, false positive
defects are eliminated based on HVS.
2 The Characteristics of <strong>TFT</strong>-<strong>LCD</strong> <strong>Image</strong>
Inherently <strong>TFT</strong>-<strong>LCD</strong> panel is non-light emitting
display devices, thus <strong>the</strong>y need an external light source
called BLU. There have been many ef<strong>for</strong>ts to make BLU as
ideal plane light sources so that <strong>the</strong> illumination from <strong>the</strong>
frontal panel can be uni<strong>for</strong>m [7]. Because <strong>the</strong> BLU is placed
at <strong>the</strong> panel border, <strong>the</strong> light from <strong>the</strong> source would decrease
according to <strong>the</strong> distance. To make <strong>the</strong> light more uni<strong>for</strong>mly
distributed several additional sheets such as diffuser sheet,
horizontal and vertical prism sheets are required. Through
<strong>the</strong>se sheets a light can be diffused and be concentrated.
Examples of a BLU structure and highlighted non-uni<strong>for</strong>m
light transmission from <strong>the</strong> source are shown in Fig. 2.
(a)
Gray Level
(b)
Fig. 2. The structure of a BLU and light transmission from <strong>the</strong> light source
(a) The BLU structure (b) light emitting from a light source.
(a)
(b)
Line Profiles
200 Line
183 Line
190
180
170
160
150
140
130
120
110
100
90
0 50 100 150 200 250
Position
(c)
Fig.3. Sample <strong>TFT</strong>-<strong>LCD</strong> image with Mura defects and its characteristics.
(a) The <strong>TFT</strong>-<strong>LCD</strong> image with Mura defects, (b) signal generated <strong>TFT</strong>-<strong>LCD</strong>
image having Muras of various size and strength and (c) line profiles of
dotted circle and arrow.

The light is diffused and concentrated through <strong>the</strong>se sheets.
But <strong>the</strong>re still exist some illumination variations measured
by LU of more than 80%. Above 80% LU is considered
acceptable in industry. The luminance values <strong>for</strong> calculating
<strong>the</strong> LU are acquired from <strong>the</strong> predetermined positions.
Mura defects are darker or brighter than <strong>the</strong>ir neighboring
normal regions in <strong>TFT</strong>-<strong>LCD</strong> images. They have various
shapes and strengths according to <strong>the</strong> cause of generation.
Some of <strong>the</strong>m occur when unexpected <strong>for</strong>eign materials are
inserted into <strong>the</strong> layers in a display panel and <strong>the</strong>y can be
generated due to non-uni<strong>for</strong>m liquid crystal distribution, air
insertion, and partial press by external <strong>for</strong>ces, etc [8].
Because of <strong>the</strong> non-uni<strong>for</strong>m illumination characteristic of <strong>the</strong>
background signal, dark Mura can be brighter than <strong>the</strong>
normal background region. Two of our experimental <strong>TFT</strong>-
<strong>LCD</strong> panel images are shown in Fig. 3(a), (b). Fig. 3(a)
includes 4 White and Dark Mura defects and Fig. 3(b) has
<strong>the</strong> signal generated Muras with various size and strength.
The position of Muras are encircled or marked with arrows.
The line profile of dotted circle and arrow is shown in Fig.3
(c). From <strong>the</strong>se figures, <strong>the</strong> illumination of <strong>TFT</strong>-<strong>LCD</strong> image
shows local and global variations at <strong>the</strong> same time.
3 Proposed <strong>Defect</strong> Detection Algorithm
No
Not Mura
Input <strong>Image</strong>
Discrete Cosine Trans<strong>for</strong>m
Adapted CSF Filtering
Inverse
Discrete Cosine Trans<strong>for</strong>m
Polynomial Regression Analysis
Tri-modal Thresholding
Labeling
L
if C
L
Result <strong>Image</strong>
Mura
Fig.4. The proposed block diagram <strong>for</strong> <strong>TFT</strong>-<strong>LCD</strong> image defect detection
Yes
The flow chart of <strong>the</strong> proposed algorithm is seen in Fig.
4. First, <strong>the</strong> input image is DCT trans<strong>for</strong>med and <strong>the</strong>n
proposed ACSF filter is applied to enlarge <strong>the</strong> Mura signal.
After <strong>the</strong> signal is inverse Discrete Cosine Trans<strong>for</strong>med,
polynomial regression (PR) process is employed <strong>for</strong>
flattening. Then <strong>the</strong> enhanced image is segmented by trimodal
thresholding to find White and Dark Mura at once.
To leave out reliable defects, false defects are eliminated if
<strong>the</strong> mean gray level of a defect does not have much
difference from <strong>the</strong>ir background.
3.1 Adapted CSF in DCT Domain
The human visual system(HVS) has different
frequency sensitivity and that is ma<strong>the</strong>matically represented
as CSF given by <strong>the</strong> experiment[8][9][10]. The HVS can
detect more easily <strong>the</strong> difference varying in low frequency
than high frequency. The CSF can be expressed by a
function and CSF graph is shown in Fig. 5.
Where,
1.
1
H( f ) 2.
6(
0.
192 0.
114 f ) exp( (
0.
114 f ) ) (2)
f f f is <strong>the</strong> frequency having unit of
2
x
2
y
cycle/degree. HVS have best sensitivity around
8[cycles/degree] and above 40[cycles/degree] its value
become smaller and smaller.
CSF Value
1.4
1.2
1
0.8
0.6
0.4
0.2
0
CSF Graph
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60
Frequency[Cycle/Degree]
Fig.5. The contrast sensitivity function graph.
There have been many ef<strong>for</strong>ts to use CSF to <strong>the</strong>ir
applications, especially in compression. In order to use <strong>the</strong>
CSF in real application relative sensitivity value is
important[10]. Without loss of generality, <strong>the</strong> maximum
CSF frequency can be normalized as 2 in DCT domain.
The DCT has good energy compaction property and it has
also fast algorithm. The DCT has no phase in<strong>for</strong>mation, it
only consider <strong>the</strong> magnitude of trans<strong>for</strong>med data. The CSF
has only magnitude, so it will be enough to consider <strong>the</strong>
proposed ACSF in DCT domain. The proposed adapted CSF
in DCT is ma<strong>the</strong>matically expressed by <strong>the</strong> followings.

H(
f / 64
2
)
ACSF( f Nor )
H(
0)
Where, H ( 0)
is <strong>the</strong> constant value having 0.4992 and <strong>the</strong>
adapted 1-d and 3-d CSF are shown in Fig. 6.
Value
2.5
2
1.5
1
0.5
0
0
Proposed Adapted CSF in DCT Domain
Frequency
(a)
(b)
Fig. 6. The adapted CSF in DCT domain. (a) 1-d ACSF, (b) 3-d ACSF.
The proposed ACSF is low frequency boost and high
frequency reduction filter shape. This filter enlarge Mura
signal region approximately 2 times and it also enlarge
background signal fluctuation a little. As <strong>the</strong> LU is
decreased, <strong>the</strong> background signal is more magnified so level
adjustment process must be needed. The following section
describes how to flat <strong>the</strong> emphasized signal.
3.2 Polynomial Regression
In general, regression method is used to trace general
tendency of <strong>the</strong> signal. This method represents its target
signal with best fitting function in mean square error (MSE)
point. Out of trace signal such as Mura signal in <strong>TFT</strong>-<strong>LCD</strong>
image does not much affect to find degenerated signal. Each
line of <strong>TFT</strong>-<strong>LCD</strong> image can be degenerated by
2
(3)
f
B
R
M
f ( x,
y)
x
1or
y 1
( x,
y)
M
( fˆ
m
R ( x,
y)
f ( x,
y))
(4)
Where, f ( x,
y)
B is biased PR function, R f ( x,
y)
is original
signal, fˆ ( x,
y)
m is m th order estimated PR function and
R
M is <strong>the</strong> number of pixels.
The PR in horizontal direction is expressed as
m
m
i
fˆ
( x,
y)
a x
(5)
R
i0
Where, a is i th order weight value, m is PR estimated
i
order and is <strong>the</strong> error.
MSE is used to find <strong>the</strong> best fitting line. One horizontal- line
MSE, E, is expressed to <strong>the</strong> following Eq. (6)
E
n
i0
2
n
m
m
f x,
y)
fˆ
R ( x,
y)
f ( x,
y)
i
k
( ak
xi
(6)
i0
k0
In order to find minimizing E , partial differentiation
employed given by
E
a
j
n
i0
n
i0
2
f ( x,
y)
f ( x ) x
i
j
i i
The equation is expressed as
n
j
f ( xi
) x i i
i0
a
n
a
m
m
k0
n
a
k
k0
i0
jk
xi
a
m
k j
ak
xi
(
xi
)
x
jk
i
j
xi
a1
0
i0
i0
k
i0
i0
Matrix representation is given by
n
x
x
i
m
i
x
x
x
i
2
i
m1
i
n
x
x
m
i
m1
i
x
2m
i
n
x
n
jm
i
0
x
j1
i
0
a
0
a1
am
2
E is
a
i
(7)
(8)
f ( xi
)
f ( xi
) xi
(9)
m
f ( xi
) xi
Gauss-Jordan elimination is used to find <strong>the</strong> polynomial
coefficient a . i
3.3 Tri-modal Thresholding
After <strong>the</strong> <strong>TFT</strong>-<strong>LCD</strong> image is enhanced, tri-modal
Thresholding [3] is used to segment Dark and Bright Mura

at once. The tri-modal thresholding function fT ( x,
y)
is
given by
f T
255
if ( f ( x,
y)
m k
)
( x,
y)
128
else if ( f ( x,
y)
m k
)
0
else
(10)
Where, f ( x,
y)
is <strong>the</strong> gray level at position at ( x , y)
, m ,
are <strong>the</strong> mean and standard deviation respectively and k is
controlled constant to access <strong>the</strong> amounts of abnormal gray
level. After tri-modal thresholding <strong>the</strong> Bright Muras have
value of 255, <strong>the</strong> Dark Muras have 128 and background
region have value of 0.
3.4 <strong>Defect</strong> Analysis
The segmented result can have false defects such noise,
neglectful small region, etc. So reliable defect confirmation,
defect analysis process is needed. In this paper, HVS based
false region elimination method is proposed. The noticeable
Luminance difference is given by <strong>the</strong> Weber Constant C Weber
in <strong>the</strong> Weber region as <strong>the</strong> following Equation (11) [11].
C W eber
L
(11)
L
Where, L, L
are <strong>the</strong> background region luminance and <strong>the</strong>
luminance differences between <strong>the</strong> background region and
defect region. We use C value as 0.02 in this paper.
Weber
4 Experimental Results
The proposed method is tested by using <strong>the</strong> 160 real
Muras and 40 generated ones. The acquired <strong>TFT</strong>-<strong>LCD</strong>
images have 400 m spatial resolution <strong>for</strong> each pixel and an
8-bit brightness resolution. To evaluate <strong>the</strong> per<strong>for</strong>mance of
proposed algorithm, one dimensional line profile, <strong>the</strong>
enhanced result image and final segmented Muras along
with detection accuracy table are shown in this paper.
The proposed defect detection results are shown in Fig. 7.
The test image Fig. 7(a) has artificially generated Muras
with various strengths and sizes. In <strong>the</strong> enhanced result
image, Fig. 7(b), <strong>the</strong> defect is more visible than original
image and that are evident in Fig.7 (e),(h),(k) also. The
original image of Fig. 7(g) has much background variation,
so <strong>the</strong> defects are rarely seen in <strong>the</strong> original image. But <strong>the</strong>
enhanced image has relatively flattening background and <strong>the</strong>
defects are more salient. In <strong>the</strong> test image Fig. 7(j), novel
enhancement result is given and that helps final
segmentation result.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
Fig. 7 The results of proposed methods. (a),(d),(g),(j) are test images,
(b),(e),(h),(k) are enhanced results and (c),(f),(i),(l) are final segmented
results of original image (a),(d),(g),(j) respectively.
The microscopic enhancement results by <strong>the</strong> line profiles are
shown in Fig. 8. Fig. 8 is <strong>the</strong> enhancement results’ profiles
of Fig. 7. By that, <strong>the</strong> effectiveness of our algorithm is seen
more clearly. In <strong>the</strong> filtered line profile results, our proposed
enhancement method can flatten <strong>the</strong> background signal
fluctuation and emphasize <strong>the</strong> defect region.
The final per<strong>for</strong>mance results are summarized in Table 1.
false-positive and false-negative measure are used to
evaluate <strong>the</strong> accuracy of defect detection result. There are
some problems to <strong>the</strong> elimination of defects existing at <strong>the</strong>
boundary region and very weak-small-size defects. Except
such cases, <strong>the</strong> false-positive rate and false-negative rate are
quite acceptable.
Table 1. Experimental results <strong>for</strong> <strong>the</strong> detection of <strong>the</strong>
Muras.
Number of
<strong>Defect</strong>s
False-positive Falsenegative
Artificially
Generated Mura
160 0.04 0.03
Real Mura 40 0.03 0.02

Gray Level
Gray Level
Gray Level
Gray Level
150
140
130
120
110
100
Line Profiles
Original 183 Line
Enhanced 183 Line
90
0 50 100 150 200 250
110
105
100
95
90
85
Position
(a)
Line Profiles
Original 95 Line
Enhanced 95 line
80
0 50 100 150 200 250
190
180
170
160
150
140
130
Position
(b)
Line Profiles
Original 200 Line
Enhanced 200 Line
120
0 50 100 150 200 250
200
190
180
170
160
Position
(c)
Line Profiles
Original 128 Line
Enhanced 128 Line
150
0 50 100 150 200 250
Position
(d)
Fig. 8 The line profiles of original image and enhanced result. (a),(b),(c),(d)
are line profiles of Fig. 7(a),(d),(g),(j) and Fig. 7(b),(e),(h),(k) respectively.
5 Conclusions
In this paper, a novel defect detection algorithm in <strong>TFT</strong>-
<strong>LCD</strong> image with <strong>the</strong> guidance of enhanced image using
ACSF and PR is proposed. The ACSF in DCT domain is
used to emphasize defect region while PR is employed to
level <strong>the</strong> background signal. Tri-modal thresholding
segmentation method is utilized in order to find White Mura
and Dark Mura at <strong>the</strong> same time. The defect candidates are
given by <strong>the</strong> distant gray level from <strong>the</strong> mean. For reliable
defect confirmation, Weber’s Constant is used <strong>for</strong> <strong>the</strong>
purpose of eliminating false detected defects.
The effectiveness of our methods is tested by <strong>the</strong> two points.
One is image enhancement and <strong>the</strong> o<strong>the</strong>r is final
segmentation result. Mura signal is clearly enlarged along
with background signal flatten. The final defect detection
results are evaluated by false-positive and false-negative
measure. By <strong>the</strong> experimental results our proposed method
has novel result in finding defects in <strong>TFT</strong>-<strong>LCD</strong> image and it
can be applicable to similar FPD images.
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