• Journal of the Korean Vacuum Society
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• v.9 no.4
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• pp.328-334
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• 2000

DLC (Diamond-Like Carbon) films were deposited by ECR-PECVD (electron cyclotron resonance plasma-enhanced chemical vapor deposition) method with the variation of substrate bias voltage under the others are constant except it. We have investigated the ion bombardment effect induced by the substrate bias voltage on films during the deposition of film. The characteristics of the film were analyzed using the Dektak surface profiler, SEM, FTIR spectroscopy, Raman spectroscopy and Nano Indentation tester. FTIR spectroscopy analysis shows that the amount of dehydrogenation in films was increased with the increase of substrate bias voltage and films thickness was decreased. Raman scattering analysis shows that integrated intensity ratio $(I_D /I_G)$ of the D and G peak was increased as the substrate bias voltage increased, and films hardness was increased. From these results, it can be concluded that films deposited at this experimental have the enhanced characteristics of DLC because of the ion bombardment effect on films during the deposition of film.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.224-231
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• 2001

Hydrogenated amorphous carbon films were deposited by ERC-PECVD with deposition conditions, such as ECR power, gas composition of methane and hydrogen, deposition time, and substrate bias voltage. The characteristics of the film were analyzed using the AES, ERDA, FTIR. Raman spectroscopy and micro hardness tester. From the results of AES and ERDA, the elements in the deposited film were confirmed as carbon and hydrogen atoms. FTIR spectroscopy analysis shows that the atomic bonding structure of a-C:H film consisted of sp³and sp²bonding, most of which is composed of sp³bonding. The structure of the a-C:H films changed from CH₃bonding to CH₂or CH bonding as deposition time increased. We also found that the amount of dehydrogenation in a-C:H films was increased as the bias voltage increased. Raman scattering analysis shows that integrated intensity ratio (I/sub D//I/sub G/) of the D and G peak was increased as the substrate bias voltage increased, and films hardness was increased.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.817-822
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• 2006

Diamonds have been widely employed as polishing media for precise machining and noble substrates for microelectronics. The recent development of the split sphere press has led to the enhancement of low quality natural diamonds. Synthesized and treated diamonds are sometimes traded deceptively as high quality natural diamonds because it is hard to distinguish among these diamonds with conventional gemological characterization method. Therefore, we need to develop a new identification method that is cheap, fast, and non-destructive. We proposed using a new method of micro-Raman spectroscopy for checking the local HPHT residual stress to distinguish these diamonds from natural ones. We observe unique ~10f compressive and tensile strains at Type I and Type II diamonds after HPHT treatment. Our result implies that our proposed methods may be appropriate fur identification of the treated diamonds with appropriate reference samples.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.264-271
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• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• Proceedings of the Korea Crystallographic Association Conference
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• 2001.05a
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• pp.21-21
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• 2001

• Electrical & Electronic Materials
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• v.30 no.1
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• pp.13-21
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• 2017

• Proceedings of the Korean Society of Potoscience Conference
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• 2006.06a
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• pp.43-43
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• 2006

• Proceedings of the Korean Society of Potoscience Conference
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• 2005.06a
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• pp.73-73
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• 2005

• Bulletin of the Korean Chemical Society
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• v.19 no.8
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• pp.885-888
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• 1998

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.467-470
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• 2004

Diamond-Like Carbon (DLC) thin film is a semiconductor with high mechanical hardness, low friction coefficient, high chemical inertness, and optical transparency. DLC thin films have widespread applications as protective coatings and solid lubricant coatings in areas such as Hard Disk Drive (HDD) and Micro-Electro-Mechanical-Systems (MEMS). In this work, the wear characteristics of DLC thin films deposited on silicon substrates using a DC-magnetron sputtering system were analyzed. The wear tracks were measured with an Atomic Force Microscope (AFM). To identify the sp2 and sp3 hybridization of carbon bonds and other bonds Raman spectroscopy was used. The structural information of DLC thin films was obtained with Fourier transform infrared spectroscopy and wear tests were conducted by using a micro-pin-on-reciprocator tester. Results showed that the wear characteristics were dependent on the sputtering conditions. The wear rate could be correlated with the bonding state of the DLC thin film.