Automated Visual Inspection System for Mass Production of Hard Disk Drive Media

Available online at www.sciencedirect.com

Procedia Engineering 41 (2012) 450 – 457

International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)

Automated Visual Inspection System for Mass Production of Hard Disk Drive Media Zhi Sheng Chowa, Melanie Po-Leen Ooia*, Ye Chow Kuanga, Serge Demidenkob a

School of Engineering, Monash University, Sunway Campus, Bandar Sunway, 46150 Selangor Darul Ehsan, Malaysia b Centre of Technology, RMIT International University Vietnam, Ho Chi Minh City, Vietnam

Abstract Manual visual inspection is currently used in the modern Hard Disk assembly process. This research shows a feasible design to automate the visual inspection process based on wavelength dependant detection. Two defect detection algorithms, one - computationally simple and another - complex, are explored in this paper. The developed system meets the performance of the current manual inspection method while providing high accuracy in targeted defects.

© 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Centre of Humanoid Robots and Bio-Sensor (HuRoBs), Faculty of Mechanical Engineering, Universiti Teknologi MARA. Keywords: Visual Media Inspection; Hard Disk Drive Media; Machine Vision; Industrial Automation

1. Introduction The Hard Disk Drive (HDD) media forms the most critical component of the HDD as all data are stored, written to and read from it. In HDD production, the media must be carefully inspected and tested [1]. HDDs that have failed the final quality and functional testing are sent to a Teardown stage whereby they are disassembled to their major components. HDD media from the Teardown stage must be retested and re-inspected. Good media componets will be recycled back into the production process, while faulty units will be sent for scrapping. Current industrial method for HDD media surface inspection is based on the use of a Laser Doppler Vibrometry (LDV) that measures the frequency shift between a reference laser beam and a test response beam [2]. It is highly accurate and capable of detecting faults of down to 1μ m size on the surface of a HDD media. However the use of LDV incurs a long inspection time, taking up to 50 seconds to fully inspect one HDD media [3]. To increase the test throughput, multiple LDV systems must be purchased so to operate in parallel. However modern LDV systems could cost more than USD50,000 per unit, excluding depreciation and maintenance [2], thus making it an expensive option. A far cheaper and effective option is to place a preliminary Visual Manual Inspection (VMI) stage prior to the LDV system. Fig. 1(a) shows the original inspection process without VMI. The amount of time that it takes for media to be rejected is 50 seconds. On Fig. 1(b), the VMI stage allows for much faster screening of defective media of 2 seconds. Additionally, on average, there will be a lower number of media processed by the LDV, which would speeds up the overall inspection time. This is advantageous in the Teardown situation (which is where faulty assembled HDDs end up) because the probability of encountering a defective media there is relatively high. Unfortunately, the manual inspection by human operators leads to such problems as: • Inconsistency of results – subjective evaluation by different operators may yield different results;

* Corresponding author. Tel.: +6-035-514-6238; fax: +6-035-514-6207. E-mail address: [email protected]

1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.07.197

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

• • •

Yield loss due to lack of automation– human operators manually handle the media, which, if done improper, may lead to damaging the sensitive media; Inefficient data logging – manual data logging tends to be far slower and inconsistent ; High cost of staff training– due to staff turnover in the company new operators need to be trained to acquire sufficient skills and experience to carry out the inspection. (a) W ithout VMI 50 seconds HDD Media from Teardown

LDV Inspection of Media

Pass

Media Reused

Fail

Media Scrapped

(b) W ith VMI

HDD Media from Teardown

2 seconds

50 seconds

VMI of Media

LDV Inspection of Media

Pass

Fail

Pass

Media Reused

Fail

Media Scrapped

Media Scrapped

Fig.1. HDD media post-disassembly inspection process (a) without VMI and (b) with VMI

For these reasons, it would be desirable to replace the human inspection with an automated vision inspection. Historically, automatic visual inspection systems have tended towards sophisticated image post-processing aiming to extract salient features from the acquired images [4]. This increases the computational time required to process each image [1]. For example, [1] proposed a solution for accurately identifying and detecting defects on the media surface, but with a high inspection time of 2 minutes/surface. An alternative approach is to improve the image acquisition instrumentation such that the image post-processing stage would be greatly simplified. This paper presents a low-cost alternative approach to improve the image acquisition instrumentation for HDD media surface inspection by making use of optical wavelength dependency. It takes an advantage of using the optical properties of the media as well as its surface defects to improve quality of the acquired images. Spectral imaging is based on a phenomena that information obtained from the spectral transmittance/reflectance to/from the object under investigation can vary at different wavelengths of light [5]. Thus, the image acquisition instrumentation can be designed in such a way so as to maximize the contrast between defective and non-defective regions while utilizing the principle of spectral imaging. This paper proposes to employ spectral imaging method and appropriate image acquisition instrumentation system to acquire media surface images with a higher contrast between the surface defects and background. This allows increasing the defect detection accuracy while keeping the software-based processing simple and fast. In turn this will lead to shorter inspection time. The paper is structured as follows. Section 2 presents the image acquisition instrumentation system used in this research. Section 3 describes the image processing algorithms for defect cluster detection and classification. Section 4 describes the experimental setup, presents the obtained results and discusses specifics and potential application of spectral imaging to automated media surface defects detection and classification with the required high accuracy. This is followed by Conclusion and Acknowledgements. 2. Image acquisition system specifications In order to assess the effects of different light wavelengths on the contrast between media surface defects and its background, a prototype setup shown in Fig. 2 was designed and built. The components used for building the system must be compatible with the requirements to the Class 100 cleanroom, which is the location of the industrial experimental setup for media inspection. The individual components of the system are described in detail in the following subsection. 2.1 Camera Specifications A Nikon D90 DSLR camera with a 12.1 megapixels APS-C (23.6x13.4mm) size sensor was used. Pixel pitch of the sensor was 5.5μ m. The lens used in the research was a Tamron 17-50mm f/2.8 zoom employed at the 50mm focal length. The aperture was set to f/8 for optimal resolving power via IMATEST [6]. The image acquisition system shown in Fig. 2

451

452

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

allows for the reliable detection of defects as small as 36.19 μ m. This has been determined by measuring the lowest level of detail at high magnification via image processing tools.

Fig. 2. Illustration of the media image acquisition system

2.2 Lighting Specifications The Dark Field lighting configuration was employed to eliminate specular reflections from the HDD media surface. This is because the HDD media surface is highly polished (RMS roughness does not exceed 90 angstroms). As a result the surface of the HDD media is extremely reflective in nature [1]. Any illumination method other than dark field lighting will cause excessive light reflection by the media surface. This would occlude the appearance of any defects on the media, hence making accurate detection of defects impossible [7]. The light source was the VAOL-5GWY4 high intensity white LED. This bright light source was chosen based on its low-cost, long lifespan, and broad spectral characteristics (from 400nm to 750nm within the visible spectrum as been found in [7]. 2.3 Optical filter specifications Spectral imaging was achieved through the use of optical bandpass filters helping to isolate the information obtained from each wavelength of light [8]. This allowed for image acquisition at specific wavelengths of the spectrum region. Each filter is based on a thin film Fabry-Perot interferometer, which permits for the transmission of a 10nm bandwidth of light and rejects all other unwanted radiation. In total 23 optical filters were used in the research ranging from 440nm to 660nm wavelength spectra with 10 nm step intervals. 3. Image processing for defect cluster detection and classification 3.1 Defect clusters on HDD Media A cluster is loosely defined as an occurrence of either extremely similar or completely identical elements in close spatial proximity [9]. Clustering is the method used to group these elements together. There are four types of surface defects commonly found on the HDD media (Fig. 3) [7].

(a) (b) (c) (d) Fig. 3.Surface defects on HDD Media: (a) Scratch, (b) Ding, (c) Glove Mark and (d) Particle Contamination

Scratch is a long surface indentation caused by faulty mechanical handling. Ding is a “hole” on the media surface caused by a faulty head stack that bumps against the media surface. Glove mark is a group of powder particles that were transferred from a clean room glove to the media surface by erroneous handling by a human operator. Particle Contamination is excessive random dust particles resting on the media surface. 3.2 Defect clustering algorithms Statistical partitioning clustering algorithms such as k-means [10], k-medoid [11] and mean-shift [12] are widely used for unsupervised cluster extraction across the different research fields and applications. Mean-shift clustering in particular has a huge advantage over k-means and k-medoid, in that it does not require prior knowledge on number of clusters and is capable of determining the distance between clusters [12]. It operates by recursively searching for the direction of the maximum increase in density in an agglomerative hierarchical manner. The mean-shift algorithm requires only geometrical

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

453

coordinates and density value, thus leading to a very small input set. Therefore theoretically its implementation is computationally fast. Because of these key advantages, mean-shift clustering was short-listed for implementation in this research. The implemented algorithm is discussed below in Section 3.3. In computer vision, cluster extraction can also be achieved by image segmentation partitioning methods such as connected-components labelling and nearest-neighbour clustering [13][14][15] The nearest neighbour clustering algorithm begins by first assuming that all points are in individual clusters [13]. It then calculates the shortest distance between two points from different clusters and merges them if the distance is less than a pre-specified threshold. Although this method is fast and easy to implement, it only works in discrete positions, which limits its application on continuous dataset. The connected-components labelling can be implemented by simply pairing adjacent “1”s and “0”s in a binary image [14] [15] until all data belongs to some cluster. Latest development in connected-components labelling algorithms [16] [17] [18] has made its implementation very fast, and thus it is extremely attractive for real-time cluster segmentation in this research. This algorithm was further developed for implementation in Section 3.4. The Scratch defect is particular interesting, as it is an elongated surface indentation that can be detected and classified using Radon transform, a popular image processing technique known for extracting line features effectively in a noisy image [19]. The Radon transform is the projection of the image intensity along a radial line oriented at a specific angle. By defining an arbitrary function on two dimensional space, the mapping of the Radon Transform is the projection of the line integral of the function along all possible lines L as illustrated in [20]. This transform is used in Section 3.4 for scratch classification. 3.3 Mean-shift clustering for HDD media surface defect detection and classification This research uses the hierarchical blurring mean-shift algorithm [21], and a kernel density estimator shown in (1), whereby is the bandwidth. The mean-shift cluster is determined by π, whereby a larger π tends to merging small, discontinuous clusters together while a smaller π tends to fragment large clusters. In this research, the optimal value for π is determined by automatically selecting the best clustering result at each hierarchy using a cluster validity index. (1) The cluster validity index used is separation and distance, which is the SD validity index [22]. It is a standard method that combines the separation and distance indexes and is computed using (2), where a is the weighting factor equal to and is the maximum number of input clusters. The average scattering for clusters can be computed by (3), where is the total number of clusters, is the standard deviation among individual clusters and is the overall standard deviation of the data set. The total separation between clusters can be defined as (4) where is the maximum distance between cluster centres and is the minimum distance between cluster centres. The mean-shift clustering algorithm is shown in Fig. 4(a). (2) (3) (4) 3.4 Connected-components clustering for HDD media surface defect detection and classification The algorithm for image thresholding and connected-component labeling is shown in Fig. 4(b), whereby detection results are obtained in “Out1”. From [1], the TB value was empirically found to be governed by equation (5), whereby TO is the Exposure that is automatically extracted using Otsu’s method [23]. TB = TO + 0.0706

(5)

Thus, any luminance value equal or above TB is set to “1”, while any values below it will be set to “0”. If there are no surface defects on the HDD media, there would not be any pixels having value “1” after the thresholding. Thus, positive detection of a defect is obtained if a non-zero dataset is returned after the conversion to a bitmap. Detected defects are then sent for Spot and Scratch classification. Dings, particle contamination and glove marks will show up as positive classification at the Spot Classification module output, while scratches will show positive identification at the Scratch Classification module output. In the Spot classification module, connected-component labeling technique is used to perform clustering. This technique is used to identify and quantify the size of groups of pixels having value “1”. Table 1 shows the threshold values Td used for classifying dings, particles and glove marks based on their sizes 7.

454

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

For scratches, knowledge on the cluster size alone is insufficient to perform classification. These defect types require line detection to extract two-dimensional characteristics. Thus, the Radon Transform was used. The threshold values TSC used in this research is shown in Table 2 [7]. It can be seen from Table 2 that Radon transform cannot be used to distinguish between particle contamination and dings, but it can distinguish scratches. In: HDD Media Image

In: HDD Media Image

π = pmin

Luminance Extraction

Perform mean-shift clustering

Bitmap thresholding, T B Increment π

Is π > πmax Yes

TB > 0 Scratch Classification

Use the SD validity index to obtain best clustering results

No Defects

Yes Spot Classification

Radon Transform Choose the π value Validate the clustering results

No

No

Out2: Defect Classification

Scratch Thresholding, TSC

Connected Components Labeling

Out1: Defect Detection

Spot Thresholding, Td

Out2: Defect Classification

Out1: Defect Detection

(a) (b) Fig. 4. The clustering algorithms used in this research: (a) mean-shift algorithm used for cluster detection and (b) Thresholding and Connected components labeling defect detection and classification algorithm Table 1. Spot Classification threshold values for Td Defect Type Td (μ m) Ding 70-80 Particle Contamination <70 Glove Mark >500 Table 2. Scratch Classification threshold values for TSC Defect Type TSC (μ m) Scratch 40 Particle Contamination and Dings 0

4. Experimental Setup, Results and Discussions 4.1 Experimental Setup Large scale industrial experimentation was performed at a manufacturing plant. The Teardown stage was selected for the experiment, which was performed in a Class-100 Clean room (i.e., no more than 100 particles per cubic foot of air present, and the premises adhere to the ISO 14644-1 Class 5 standard [24]). A sample of 40 defective HDD media was used to perform the spectral imaging and clustering experiment. Among the selected samples, 10 were with glove marks, 10 with particle contamination, 5 with scratches, 5 with dings and 10 were non-defective. The image acquisition system presented in Section 2 was used to acquire the HDD media samples. There are two surfaces for every HDD media (i.e. the top side and bottom sides are SIDE A and SIDE B respectively). Hence each side must be imaged separately, thus doubling the total media surface samples to 80. The procedure for image acquisition is provided in Fig. 5. 4.2 Defect-background contrast and its wavelength dependency Using the image acquisition procedure shown in Fig. 5, the full RGB and spectral images of each HDD media surface were logged into the system. The spectral imaging experiment showed that there were visible differences in the defectbackground contrast for some types of defects. For example, Fig. 6 shows a finger mark on the HDD media surface whereby the area of interest has been cropped out for illustration purposes in this paper. It can be observed that the highest visibility of the defect is in the 600-660nm spectral images. The defect appears completely invisible to the naked eye at the 510580nm spectral images. Thus, there is an observed dependency between the defect-background contrast and the wavelength of imaging. The quantitative effect of spectral imaging is then studied using thresholding and connected-components labeling algorithm shown in Fig. 4(b), which is applied on every HDD media surface. Fig. 7(a) and (b) shows the false positive and false negative results respectively for each spectral wavelength as well as the full RGB images. RGB images are observed to

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

result in higher misclassification rates, whereby the ding, glove mark and scratch defect types tend to show up as particulate defects. This manifests as high false negative rates for ding, glovemark and scratch defects in Fig. 7(a), which corresponds to the high false positive rate of particles in Fig. 7(b). The 450-510nm wavelengths are best at detecting particles, glovemarks and scratches (whereby the false positive and false negative rates have the best tradeoff), while 540-570nm wavelengths have the optimal tradeoff for detection of dings. The optimal imaging wavelength is 450nm, which gives the best classification rate for all the surface defect types. Select HDD Media Sample, n = 1, SIDE = A

Acquire RGB image without optical filter, store into Sample nSIDE database

Set Filter = 440nm Acquire Image, store into Sample nSIDE database

Is Filter = 660nm

Filter = Filter + 10nm

No

Yes

SIDE =B

No

nSIDE= nB? Yes n=n+1

No

n = nmax? Yes Perform Clustering

Fig. 5. Image acquisition procedure, whereby nMAX refers to the maximum number of HDD media samples provided by Western Digital Malaysia for the experiment 440nmA

500nm

560nm

620nm

450nm

510nm

570nm

630nm

460nm

520nm

580nm

640nm

470nm

480nm

490nm

530nm

540nm

550nm

590nm

650nm

600nm

610nm

660nm

Fig. 6. An example of the spectral imaging effect on the defect-background contrast

(a)

(b)

Fig. 7 Results of classification against spectral wavelength for (a) false negative (b) false positive

455

456

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

4.2 Comparison of the suitable defect clustering and classification algorithm With the selected spectral wavelength of 450nm, the defect clustering algorithms shown in Fig. 4(a) and (b) were compared for performance. The mean-shift clustering algorithm shown in Fig. 4(a) was applied to experimental sample set. Fig. 8 shows an example of dust particles that are detected by mean-shift clustering. The classification results are shown in Fig. 9, whereby it is observed that 100% accuracy was achieved for detection and classification of every defect type except dings, which the algorithm was completely unable to identify. Another important observation from applying the mean-shift clustering algorithm was the high computational time. The computational time for varying image segment sizes is shown in Fig. 10. The size of the captured disk image was 2700 x 2826 pixels. Extrapolating the time required to fully process the image via segmenting, it was found that a full image requires 2,563 seconds to fully be processed. This is contrasted with the average of 12 seconds using thresholding and connected-components labeling algorithm. Thus the trade-off for identical defect detection performance and slightly higher defect classification accuracy is a 200 fold increase in computing time. As the goal of the research is to keep the inspection time, the thresholding and connected-components labeling could be accepted as most suitable algorithm for HDD media surface inspection.

Red – Cluster Blue – Unclustered Circle – Grouped

Fig. 8 Example of particle contamination whereby the defect clusters are circled

Fig. 9 Classification results of the mean-shift clustering

Fig. 10 Computational time of the mean-shift clustering algorithm

5. Conclusion An An automated visual inspection system was designed for HDD media. It utilizes the principles of spectral imaging at 450nm wavelength to increase the defect-background contrast and a rapid thresholding and connected-components labeling algorithm for defect detection. The system provides required high speed of operation (an overall test time per media platter side is 12s) thus not adding any substantially to the pre-existing test time. The overall test time can be further improved by using better test hardware to remove the dependency for automatic image alignment. It has been tested to be 100% accurate in detecting all specified defects on the HDD media. 6. Acknowledgements The authors would like to thank Western Digital Malaysia for their support and resources to conduct this research and the Malaysian Ministry of Higher Education for provision of the research grant FRGS/1/2011/SG/MUSM/03/1.

Zhi Sheng Chow et al. / Procedia Engineering 41 (2012) 450 – 457

References [1] L.Hepplewhite, T.J.Stonham, and R.J.Glover, "Automated Visual Inspection of Magnetic Disk Media," in ICECS '96, Rodos, Greece, 1996, pp. 732735. [2] P. Castellini, M. Martarelli, and E.P. Tomasini, "Laser Doppler Vibrometry: Development of Advanced Solutions Answering to Technology's Needs," Mechanical Systems and Signal Processing, vol. 20, no. 5, pp. 1265-1285, August 2006. [3] Jason Pressesky. (1997, January) Seagate Technology Characterizes Hard Drive Defects More Rapidly with Solution Based on NI Products. [Online]. http;//sine.ni.com/cs/app/doc/p/id/cs-378 [4] Xianghua Xie, "A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques," ELCVIA - Electronic Letters on Computer Vision and Image Analysis, vol. 7, no. 3, pp. 1-22, April 2008. [5] B.C. Wilson and S.L. Jacques, "Optical reflectance and transmittance of tissues: principles and applications," IEEE Journal of Quantum Electronics, vol. 26, no. 12, pp. 2186-2199, Dec 1990. [6] Norman Koren, "The Imatest program: comparing cameras with different amounts of sharpening," in Proc. SPIE 6069, San Jose, CA, USA, 2006, pp. 60690L-1 to 60690L-9. [7] Zhi Sheng Chow, Melanie Po-Leen Ooi, Ye Chow Kuang, and Serge Demidenko, "Low-Cost Automatic Visual Inspection System for Media in Hard Disk Drive Mass Production," in Proceedings of The IEEE Instrumentation and Measurement Technology Conference, Graz, 2012, pp. 234-239. [8] LOT-Oriel Group Europe. (2012, January) Optical filters for light sources. Document. [9] The American Heritage® Dictionary of the English. (2004) Dictionary.com. [Online]. http;//dictionary.reference.com/browse/clustering [10] R. Jornsten, Y. Vardi, and C.H. Zhang, "A Robust Clustering Method and Visualisation Tool Based on Data Depth," in Statistical Data Analysis Based on the L1-norm and Related Methods.: Birkhauser, 2002, pp. 353-366. [11] Arun K Pujari, Data Mining Techniques, 4th ed.: Orient Blackswan, 2001. [12] D Comaniciu and P Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002. [13] Huang, Chenn-Jung; Wang, Chua-Chin; Wu, Chi-Feng;, "Image processing techniques for wafer defect cluster identification," IEEE Design & Test of Computers, vol. 19, no. 2, pp. 44-48, March 2002. [14] Michael B Dillencourt, Hannan Samet, and Markku Tamminen, "A general approach to connected component labeling for arbitrary image," Journal of the ACM 39, vol. 39, no. 2, pp. 253-280, April 1992. [15] Hanan Samet and Markku Tamminen, "An improved approach to connected component labeling of images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, 1986, pp. 312-318. [16] Yang Yang and David Zhang, "A novel line scan clustering algorithm for identifying connected components in digital images," Image and Vision Computing, vol. 21, no. 5, pp. 459-472, May 2003. [17] Lifeng He, Yuyan Chao, and K. Suzuki, "A Linear-Time Two-Scan Labeling Algorithm," in Proceedings of the IEEE International Conference on Image Processing, vol. 5, San Antonio, USA, 2007, pp. 241-244. [18] Luigi D Stefano and Andrea Bulgarelli, "A simple and efficient connected components labeling algorithm," in Proceedings 10th International Conference on Image Analysis and Processing, Los Alamitos, CA, 1999, pp. 322-327. [19] Stanley R. Deans, The Radon Transform and Some of Its Applications. New York, USA: Dover Publications, Inc., 2007. [20] Peter Toft. (1997, April) The Radon Transform. [Online]. http;//cogsys.imm.dtu.dk/staff/ptoft/Radon/Radon.html [21] B.W. Silverman, Density Estimation for Statistics and Data Analysis. London, New York: Chapman & Hall, 1986. [22] B Balasko, J Abonyi, and B Feil. (2005) Fuzzy Clustering and Data Analysis Toolbox. Department of Process Engineering, University of Veszprem. [23] Nobuyuki Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-9, no. 1, pp. 62-66, January 1979. [24] International Organization for Standardization, ISO14644-1 Cleanrooms and associated controlled environments , 1999.

457