Using active thermography to inspect pin-hole defects in anti-reflective coating with k-mean clustering
Introduction
The size of a pin-hole defect is one of the dominant factors in the quality control of an anti-reflection coating because the presence of pinholes lowers the usability, degrades the functionality and negatively impacts the user satisfaction. To the best of authors׳ knowledge, many manufacturers of AR coatings used for viewing applications have set limits on the maximum tolerable pinholes size [1], [2], [3], [4]. A commonly acceptable pinhole size for such applications is 0.08–0.1 mm [1], [2], [3], [4].
Nowadays, the visual inspection of AR films is still performed manually despite the fact that machine vision technology has existed for several years; furthermore, the manual inspection of defects is tedious and may cause a hazardous working environment [1], [2], [3], [4], [5], [6], [7]. Separately, other technologies [8], [9], [10], [11], [12], [13] such as atomic force microscopy (AFM) [8], functionalized near-field scanning optical microscopy [9], and photo-thermal related technologies [10], [11] have been developed for the detection of small defects in other fields. Their cost efficiency should be considered from the viewpoint of use for AR films for viewing application. Automatic visual inspection using a charge coupled device (CCD) camera provides a much quicker and convenient method to detect defects. In this approach [14], an illumination system and a CCD camera are used to obtain multiple images. Defects are then detected based on the intensity changes caused by them. However, the images captured using the CCD camera strongly depend on the viewing angles of the illumination source as the brightness differences caused by pinholes are large enough for the CCD method to capture only within certain view angles [5], [6], [7]. The viewing angle is the largest source of error when analyzing images. To improve the inspection capabilities, Tsai [15] and Kim [16] developed two methods to enhance the contrast between the defective and the non-defective areas.
To overcome the problems faced with CCDs, thermography, which detects defects based on changes in infrared radiation caused by them, has been proposed [17], [18], [19], [20]. The attenuation coefficient of many visually transparent materials, such as PETs [4], [21], [22], [23], is large in long-wave infrared radiation [24], [25]. In other words, the IR radiation intensity through the surface of such a film may change rapidly with the film thickness. The observed IR intensity through the film combines the emitted radiance, which is related to the heat conduction, and transmitted intensity, which is related to the intensity of the illumination source. The larger the attenuation coefficient, the more significant is the emitted radiance due to heat diffusion.
This study discusses the possibility of using a thermal image to detect the defects in AR coatings for a flat panel display and proposes image processing methods to measure the diameter of defects from the millimeter to sub-pixel scales. A de-trend process is established based on statics principles behind the infrared image measurement and on heat conduction theories behind thermography testing process. k-mean clustering [[26], [27], [28], [29]] is used to distinguish the edge pixel of a defective and a non-defective area. This study shows that proposed image processing method can recognize pinhole defects with sizes from 0.03 to 4 mm and predict their diameters within 0.23 mm.
Section snippets
Experimental setup
Multilayer AR coatings for a flat panel display (Dexerials Corporation) were used as the sample. The films had an average thickness of 160 μm, and they were designed to be anti-reflective to light with wavelength of 450–670 nm [4]. The emissivity of the AR film had a value of 0.88 when tested under an ASTM code [30]. The overall transmittance of the AR film within a wavelength of 7–14 μm is 0.22 according to the ASTM code E1897–14 [31]. The samples were cut using a PLS6.120D carbon-dioxide laser
Heat conduction theories behind thermography and image processing
The pinhole defects in an AR film have different optical properties and thickness from the AR film itself. As the temperature variance over the horizontal direction is far larger than that across the depth, a lumped temperature across depth is considered. Furthermore, as the system is radially symmetric if there is no defect, the heat conduction model is reduced to one dimension:where is the temperature increment from the initial temperature of the film to the current
Image-streams based defect recognition
The original image is shown in the left-hand side of Fig. 3. Obviously, the background area was not uniform owing to non-homogenous heating. By applying the de-trend filter described in Section 3, the background was flattened (uniform with noise), as shown in the right-hand side of Fig. 3.
We noted that when the defective area was relative large (in Fig. 3, the diameter of the defective area is around one-third the width of the image), the polynomial curve may followed the trend inside the
Conclusion
This study shows the possibility of using a thermal image to detect the defects in AR coatings for a flat panel display and proposes corresponding image processing methods to measure the diameter of defects from the millimeter to the sub-pixel scales with speeds of 17 mm×13 mm/min. With de-trending, the proposed algorithm can recognize pinhole defects in the AR film with diameters as small as 0.03 mm. The proposed method suffers from a false positive and false negative rate of 2.2% each in all the
Acknowledgments
The authors sincerely thank Dexerials Corporation for providing the AR 1.5 thin films that were used as the inspected samples. The authors also thank Texas A&M University Laboratory for allowing the use of their facilities for the synthetic-biologic interactions. Finally, the authors would like to thank Mr. Richen Li.
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