Optics & Laser Technology; Vol. 29, No. 8, pp. 425±432, 1997 Published by # 1998 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0030-3992/97 $17.00 + 0.00 PII: S0030-3992(97)00047-9
Evaluation of defects on an optical disc master plate C.-S. LIN This paper presents a method to analyse and detect defects on the surface of a master disc (the cause of distorted images) by applying a neural network program and micro-image technology. The method automatically, step-by-step, detects defects in the disc's electrical signal output which drives an x±y table and provides indicative information for managing contamination in a cleanroom of a manufacturing plant. ß 1998 Published by Elsevier Science Ltd. All rights reserved. KEYWORDS: optical discs, neural networks, digital image processing
Introduction
contamination. Managing the sources, detecting, analysing and monitoring the contamination economically and eectively and selecting cleanroom material are vital for success of this industry.5 The shapes and particles in a cleaning room are classi®ed in four category types by Lieberman.6
Optical disc usage and application is not only extensively used, but growing in popularity and importance. Currently the number of optical discs manufactured by the injection method, namely CDROM and video CD, constitute about 70±80% of the total volume of optical discs produced. The future of this industry depends heavily on the quality of the ®nished product. The quality of the ®nished product is closely associated with the quality of the master plate. For this reason, it became vitally important in the production of high-density optical discs to ®nd a simple, problem-free and cost and time eective method to monitor, measure, observe and analyse the quality of the master plate on an ongoing basis. The conventional method in a production environment to detect defects on a master plate of an optical disc is to expose the plate to a yellow light and observe whether the light ®elds re¯ect any distorted images notable to the naked eye. Key points are examined using a microscope.1±3 Because of the huge surface area that has to be inspected (up to 140 (355.6 mm) diameter for CD-ROM and video CD) it is not possible for a technician to conduct in-line testing of all the master plates using a microscope. Most techniques used to observe and evaluate the microcomposition of the surface provide an earnest description of the master plates outlines. One can also observe contamination objects with a microscope, CCD camera or by using a computer to scan the dimensions of the emulsion or to detect micro-particles of a microstructure.4
1. Sphere type: the diameter of these particles is 0.01± 300 mm and the quantity about 10% of all particles. 2. Cube type: the diameter of these particles is 0.11± 1000 mm and the quantity about 30% of all particles. 3. Fibre type: the diameter of these particles is 0.1± 500 mm and the quantity of these particles is about 15% of all particles. 4. Flake type: the diameter of these particles is 0.1± 100 mm and the quantity is about 45% of all particles. The instrumentation for counting particles,7, 8 detecting and identifying the particle composition9, 10 comprises optical and photovoltage real-time monitoring measurement techniques that are essential to manage a range of cleanroom systems. This paper describes a mechanism having a re¯ecting surface used for inscribing transmitted information on an optical master disc plate in the manufacturing process. It comprises the following: . a microscope and frame; . an x±y table able to support an optical disc;
The optical disc manufacturing process is strictly evaluated on the criteria of micro-particle
. a measurement and control system for determining defects during the operation.
The author is in the Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan. Received 4 July 1997. Revised 11 September 1997.
Figure 1 highlights a block diagram of the automated, evaluating device set-up. The lamp-emitted light 425
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Evaluation of defects on an optical disc master plate: C.-S. Lin We obtain the binary images from the original, and the binary images take pixelization later. The pattern of size 16 16 binary version is divided horizontally and vertically to create to 16 subpatterns, each of size 4 4.
Fig. 1. The block diagram of the set-up.
energy is focused through the master plate onto the CCD camera at a number of pre-selected wavelengths in the red light range of 580±680 nm. The illuminating wavelength is selectively controlled by a number of ®lter elements carried (transmitted) by the respective peripheral apertures, having transmission characteristics corresponding to the pre-selected wavelengths. In conjunction with this equipment, we use a digital image processing technique and a recognition (detection) neural network algorithm as proposed in Ref.11 to look at the shape characteristics of defects on an optical disc.
Digital processing method There are numerous types of known surface defects that might occur on the copper plate of an optical disc, namely: water break, spotting out, scratching, pitting, smut etc. The grey images of an optical disc microstructure are obtained from a CCD camera. We can determine the dierence in grey level in defected and non-defected areas by analysing and comparing the grey images of aected plates. The results obtained provide an easy and eective method to detect defects. One can then proceed with a series of digital image processing techniques namely ®ltering, inverse, Laplacian, scaling, normalized and binary operations. According to the binary image version, a computer programmer could easily develop programs to distinguish the defected images from the non-defected images by grey level discrepancy. Figure 2 highlights: (a) the original image; (b) the binary image; and (c) the pixelized image. The further classi®cation of defects on an optical disc is very time consuming, so a neural network operation is needed. For example, here we show how to identify the defects of water break. A modi®ed recognition neural network is applied. The algorithm and network topology are described below. First, we introduce the network architecture. Network architecture The neural network system uses a hierarchical pattern detection scheme or system which includes both a lowand top-level pattern detection system. The low level is capable of detecting the sub-patterns. The top level detects the patterns and outline and identi®es the class to which the patterns belong.
Fig. 2. Image processing. (a) Original defect image, (b) binary image and (c) pixelized image.
Evaluation of defects on an optical disc master plate: C.-S. Lin
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If all 16 subpattern neurons ®re, they announce the detection of a pattern. However, it is possible to de®ne a reliable range of about 12 or 13 around this pattern neuron. This neuron's output then goes to the output layer which provides the classi®cation. The training patterns of the water break defects are shown in Fig. 4. Results obtained from experiments show that the networks could identify most of the water break defects on the copper plate since the true defect patterns take geometric alterations from our training patterns. We also created other training patterns to detect the defects of spotting out, scratching, pitting and smut. From the type of the defects: sphere, cube, ®bre and ¯ake, we can also ®nd the particle source and control the contamination. Fig. 3. The complete network topology of water break defect detection.
For each pattern detection neuron, there are 16 subpattern detection neurons labelled 1, 2, 3. . . 16 connected to it. They are responsible for remembering the subpatterns of training pattern grids. Each shift subpattern neuron contains a toleration limit. Figure 3 shows the complete network topology. Training algorithm The relation of an original pattern P and its subpatterns p1, p2, . . . p16 is de®ned by P = (p1, p2,..pi,..p16) and for each pi, pi = (pi1, pi2,.. pik,..pi16), 1R i, k R 16; pik={0,1}, where pik is the grey level of a pixel. The value 1 indicates that the corresponding pixel is black, while the value 0 indicates that the corresponding pixel is white. For each vector pi, there exists a weight factor wi = (wi1, wi2,.. wik,.. wi16) and a threshold yi calculated by the equations wik=2 pikÿ1; yi=S16 k=1pikwik. These weight vectors are produced for remembering the training patterns in the training phase. The threshold value yi will inhibit the neurons ®ring for all input vectors pj$ pi. That is 8, pj$ pi, pjT wi < piT wi. However, it is possible to de®ne a reliable range in order to increase the tolerance of noise. By experience, we take yi=piT wi ÿ 2.
Experiments Instead of applying a traditional method to observe these microstructures, we use an additional video camera with a multistage digital image-processing system to analyse them more quickly, powerfully and economically. The speci®cations of our device are listed as follows: 1. Dimension: 360 mm 560 mm 640 mm 2. Light source: 500±670 nm lamp 3. Image scanning device Scanning width: 10 mm Magni®cation: 140 Resolution: 256 240 4. x±y table Resolution of translation stage: 0.01 mm Speed: 450 mm/min 5. Processing speed: 5 times/second The frame grabber VIGAS is a plug-in computer card. It has a frame memory organized at 640 480 8 bits.
The proposed neural networks are guaranteed to converge in a single sweep. They provide a short time for dealing with the training phase, and the same result was seen during the identi®cation phase. Recognition methodology During the training phase, the network is presented with patterns that are recorded by the neurons at the ®rst layer. They are retained in sections with each neuron recording a 4 4 block. The same block is retained by 25 shift neurons, each recording a speci®c shift. During the recognition phase, the ®rst-layer neurons try to match the subpatterns they have retained. If any of the 25 shift neurons ®re, the second-layer neuron also ®res, announcing the detection of the subpattern. All of these 16 subpattern neurons work together to detect the complete pattern.
Fig. 4. The training patterns.
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Evaluation of defects on an optical disc master plate: C.-S. Lin
Fig. 5. The original images of the samples.
Evaluation of defects on an optical disc master plate: C.-S. Lin
Fig. 6. The original images from the testing specimen.
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Evaluation of defects on an optical disc master plate: C.-S. Lin
Table 1. The results of the operation of neural networks in training samples and test specimens Test specimen Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.
6(a) 6(b) 6(c) 6(d) 6(e) 6(f) 6(g) 6(h) 6(i)
Training sample Fig. 5(a) 13 12 7 11 12 6 12 12 11
Training sample Fig. 5(b)
Training sample Fig. 5(c)
9 9 5 8 8 8 9 8 5
18 17 10 14 14 7 12 12 11
Training sample Fig. 5(d)
Training sample Fig. 5(e)
23 21 16 19 19 7 16 15 11
17 17 13 17 17 8 16 15 10
Training sample Fig. 5(f) 17 17 14 17 17 11 14 14 11
Training sample Fig. 5(g) 18 18 15 21 22 15 17 15 12
Training sample Fig. 5(h) 17 17 11 17 17 14 20 20 14
Training sample Fig. 5(i) 15 15 11 16 16 13 21 20 22
Training sample Fig. 5(j) 17 17 11 17 17 12 20 19 13
The incoming video signal has an 8-bit resolution at a rate of 30 frames per second. An 8-bit A/D converter digitized the signal to 256 grey levels. Each pixel in the frame memory may take a value between 0 and 255, with the value 0 corresponding to the black level and 255 to the white level according to a modi®ed look-up table. The TC-4017 x±y table provided linear translating speed pulses to x±y axis translation control circuits in the unit, which provided an input to motor controllers MC 6809 associated with the motors driving the translation sieve. Each of the translation control circuits is provided with a motor-driven potentiometer providing an input into the unit. Alternatively, each of the translation control circuits may use a solid-state electronic interface between the microprocessor and the motors.
coupling to a microscope. The apparatus includes a view®nder for viewing scenes in the microscope and a CCD camera with a relay lens. The pattern re¯ected along a collimating projection lens was assembled at the CCD camera to produce a scanning pattern of light.
The CCD camera apparatus, having both automatic and manual modes of operation, is adapted for
Table 1 shows the results of novel feature recognition neural networks. The data are the number of ®ring
Fig. 7. A master plate of an optical disc.
In the training procedure, we select 10 training samples (Figs 5(a)±(j)) to create a database. We prepared nine specimens (Figs 6(a)±(i)) for testing. We observe the testing specimen (Figs 6(a)±(c)) should be identi®ed as a type of training sample (Fig. 5(d)), the testing specimen (Figs 6(d)±(f)) should be identi®ed as a type of training sample (Fig. 5(g)), and the testing specimen (Figs 6(g)±(i)) should be identi®ed as a type of training sample (Fig. 5(i)).
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Fig. 8. A series of microscopy images. (a) Perfect image, (b) pitting defect image, (c) dust defect image, (d) smut defect image, (e) spotting defected images.
neurons. The largest data, which we have marked in bold, classi®ed properly to the related patterns, so the recognition ratio is 100%. It proved that this method was able to correct mistakes made by some of the subpattern classi®ers, while maintaining the correct classi®cations for the object where there was no confusion in the algorithm outputs. It should be noted that Figs 6(c), 6(f) and 6(i) are noisy, but were still recognized correctly.
Figure 7 shows a copper plate image of an optical disc. It is a circular ring of 116 mm outer diameter and 41 mm inner diameter. From the video system we can quickly analyse an optical disc master plate within 15 min using our new software algorithms. However, if we adopt traditional pattern classes and K-mean operation, it will take 14 h to complete an optical disc. Figure 8 shows a series of microscope images of processing results during our experiments. These
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Evaluation of defects on an optical disc master plate: C.-S. Lin
patterns prove that this system can quickly and eciently detect small defects of various types in an entire optical disc master plate. From the quantity of pitting defects, dust defects, smut defects and spotting defects, one can choose to pay attention either to contamination control or the electro-forming process.
Conclusion The optical disc measurement system proved most cost-eective and worked eectively should in-line machine processing concepts be used. Our innovation allows the production of an optical disc with a more reliable system. Its application in multipurpose measurement using simple and inexpensive equipment is con®rmed in CD manufacturing.
Acknowledgements This work was sponsored by the National Science Council, Taiwan, Republic of China under grant number NSC-86-2212-E-035-003.
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