• Transactions on Electrical and Electronic Materials
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• v.15 no.5
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• pp.262-264
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• 2014

Pristine ZnO, 3 wt.% Ga-doped (3GZO) and 3 wt.% Ag-doped (3SZO) ZnO nanowires (NWs) were grown using the hot-walled pulse laser deposition (HW-PLD) technique. The doping of Ga and Ag in ZnO NWs was observed by analyzing the optical and chemical properties. We optimized the synthesis conditions, including processing temperature, time, gas flow, and distance between target and substrate for the growth of pristine and doped ZnO NWs. The diameter and length of pristine and doped ZnO NWs were controlled under 200 nm and several ${\mu}m$, respectively. Low temperature photoluminescence (PL) was performed to observe the optical property of doped NWs. We clearly observed the shift of the near band edge (NBE) emission by using low temperature PL. In the case of 3GZO and 3SZO NWs, the center photon energy of the NBE emissions shifted to low energy direction using the Burstein Moss effect. A strong donor-bound exciton peak was found in 3 GZO NWs, while an acceptor-bound exciton peak was found in 3SZO NWs. X-ray photoelectron spectroscopy (XPS) also indicated that the shift of binding energy was mainly attributed to the interaction between the metal ion and ZnO NWs.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.137-137
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• 2010

Copper electroplating is a very popular and important technology for depositing high-quality conductor interconnections, especially in through silicon via (TSV). As this advanced packaging technique developing, a mass of copper and chemical solution are used, so attention to these chemical materials into the utilization and costs can not be ignored. An economical and practical real-time chemical solution monitoring has not been achieved yet. Either Red-green-blue (RGB) or optical emission spectroscopy (OES) color sensor can successfully monitor the color condition of solution during the process. The reaction rate, uniformity and quality can map onto the color changing. Hidden Semi Markov model (HSMM) can establish mapping from the color change to upper indicators, and artificial neural network (ANN) can be integrated to comprehensively determine its targets, whether the solution inside the container can continue to use.

• KSTLE International Journal
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• v.8 no.1
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• pp.1-6
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• 2007

Chromatic device was developed using light emitting diodes, optic fibers and photodiodes. Chromatic ratio and total contamination parameters based on transmitted light intensity in Red, Green, and Blue wavelengths were used for oil chemical and particulate contamination assessment. Chromatic ratio criterion was found independent of the particulate contamination of oil; but depended on chemical degradation, being more sensitive for synthetic than mineral hydraulic oil. Total contamination index of the sensor depended on both the chemical degradation and particulate contamination of the oil; being most sensitive in blue wavelength, and least in the red. Test results for synthetic hydraulic oils monitored corroborated with results of other tests such as viscosity, total acid number, elemental optical emission spectroscopy, particulate counts and UV-VIS photospectrometry. Chromatic ratio showed a clearer indication of oil degradation, compared to key monitoring parameters such as total acid number, viscosity and particle counts. The results showed that these parameters are effective criteria for the condition monitoring of synthetic hydraulic oils.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.4
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• pp.304-309
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• 2015

To evaluate the degree of contamination caused by oil spill accident in the sea, the in-situ sensors which are based on the scientific method are needed in the real site. The sensors which are based on the fluorescence detection theory can provide the useful data, such as the concentration of oil. However these kinds of sensors commonly are composed of the ultraviolet (UV) light source such as UV mercury lamp, the multiple excitation/emission filters and the optical sensor which is mainly photomultiplier tube (PMT) type. Therefore, the size of the total sensing platform is large not suitable to be handled in the oil spill field and also the total price of it is extremely expensive. To overcome these drawbacks, we designed the fluorimeter for the oil spill detection which has compact size and cost effectiveness. Before the detail design process, we conducted the experiments to measure the excitation and emission spectrum of oils using five different kinds of crude oils and three different kinds of processed oils. And the fluorescence spectrometer were used to analyze the excitation and emission spectrum of oil samples. We have compared the spectrum results and drawn the each common spectrum regions of excitation and emission. In the experiments, we can see that the average gap between maximum excitation and emission peak wavelengths is near 50 nm for the every case. In the experiment which were fixed by the excitation wavelength of 365 nm and 405 nm, we can find out that the intensity of emission was weaker than that of 280 nm and 325 nm. So, if the light sources having the wavelength of 365 nm or 405 nm are used in the design process of fluorimeter, the optical sensor needs to have the sensitivity which can cover the weak light intensity. Through the results which were derived by the experiment, we can define the important factors which can be useful to select the effective wavelengths of light source, photo detector and filters.

• Journal of the Korean Ceramic Society
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• v.47 no.2
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• pp.189-194
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• 2010

Compared with organic fluorophores, semiconductor quantum dots (QDs) have the better properties such as photostability, narrow emission spectra coupled to tunable photoluminescent emissions and exceptional resistance to both photo bleaching and chemical degradation. In this work, CdSe/ZnS QDs nanobeads were prepared by the incorporation of CdSe/ZnS QDs with mesoporous silica to use as the optical probe for detecting toxic and bio- materials with high sensitivity, CdSe/ZnS core/shell QDs were synthesized from the precursors such as CdO and zinc stearate with the lower toxicity than pyrotic precursors. The QD-nanobeads were characterized by transmission electron microscopy, FL microscopy, UV-Vis and PL spectroscopy, respectively.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.480-480
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• 2011

양극산화(anodization)는 금속을 전기화학적으로 산화시켜 금속산화물로 만드는 기술로서 최근 다양한 크기의 나노 구조를 제조하는 기술로 각광받고 있으며, 이러한 기술에 의하여 얻어지는 anodic aluminum oxide(AAO)는 magnetic data storage, optoelectronic device, sensor에 적용될 수 있는 nano device 뿐만 아니라 nanostructure를 제조하기 위한 template 및 mask로써 최근 광범위 하게 연구되고 있다. 또한, AAO는 Al2O3의 단단한 구조를 가진 무기재료이므로 solid mask로써 다른 porous materials 보다 뛰어난 특성을 갖고 있다. 또한 electron-beam lithography 및 block co-polymer 에 의한 patterning 과 비교하여 매우 경제적이며, 재현성이 우수할 뿐만 아니라 대면적에서 나노 구조의 크기 및 형상제어가 비교적 쉽기 때문에 널리 사용되고 있다. 그러나, AAO 형성 시 생기게 되는 반구형 모양의 barrier layer는 물질(substance)과 기판과의 direct physical and electrical contact을 방해하기 때문에 해결해야 할 가장 큰 문제점 중 하나로 알려져 있다. 따라서 본 연구에서는 실리콘 기판위의 형성된 AAO의 barrier layer를 Cl/BCl3 gas mixture에서 Neutral Beam Etching (NBE)과 Ion Beam Etching (IBE) 로 각각 식각한 후 그 결과와 비교하였다. NBE와 IBE 모두 Cl2/BCl3 gas mixture에서 BCl3 gas의 첨가량이 60% 일 경우 etch rate이 가장 높게 나타났고, optical emission spectroscopy (OES)로 Cl2/BCl3 플라즈마 내의 Cl radical density와 X-ray photoelectron spectroscopy (XPS)로 AAO 표면 위를 관찰한 결과 휘발성 BOxCly의 형성이 AAO 식각에 크게 관여함을 확인 할 수 있었다. 또한, NBE와 IBE 실험한 다양한 Cl2/BCl3 gas mixture ratio 에서 AAO가 식각이 되지만, 이온빔의 경우 나노사이즈의 AAO pore의 charging에 의해 pore 아래쪽의 위치한 barrier layer를 어떤 식각조건에서도 제거하지 못하였다. 하지만, NBE에서는 BCl3-rich Cl2/BCl3 gas mixture인 식각조건에서 AAO pore에 휘발성 BOxCly를 형성하면서 barrier layer를 제거할 수 있었다.

• Journal of the Korean Applied Science and Technology
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• v.34 no.1
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• pp.50-57
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• 2017

Zinc oxide is, one of metal oxide semiconductor, harmless to human and environment-friendly. It has excellent chemical and thermal stability properties. Wurtzite-zinc oxide is a large band gap energy of 3.37 eV and high exciton binding energy of 60 meV. It can be applied to various fields, such as solar cells, degradation of the dye waste, the gas sensor. The photocatalytic activity of zinc oxide is varied according to the particle shape and change of crystallinity. Therefore, It is very important to specify the additives and the experimental variables. In this study, the zinc oxide were synthesized by using a microwave assisted hydrothermal synthesis. The precursor was used as the zinc nitrate, the pH value was controlled as 11 by NaOH. Surfactants are the ethanolamine, cetyltrimethylammonium bromide, sodium dodecyl sulfate, sorbitan monooleate was added by changing the concentration. The composite particles had the shape of a star-like, curcular cone, seed shape, flake-sphere. Physical and chemical properties of the obtained zinc oxide was characterized using x-ray diffractometer, field emission scanning electron microscopy, thermogravimetric analysis and optical properties was characterized using UV-visible spectroscopy, photoluminescence and raman spectroscopy.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Journal of IKEEE
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• v.19 no.4
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• pp.541-547
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• 2015

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in plasma etching process is more difficult than before. For endpoint detection, various kinds of sensors are installed in semiconductor manufacturing equipments, and sensor data are gathered with predefined sampling rate. Generally, detecting endpoint is performed using OES data of by-product. In this study, OES data of both by-product and etchant gas are used to improve reliability of endpoint detection. For the OES data pre-processing, a combination of Signal to Noise Ratio (SNR) and Principal Component Analysis (PCA),are used. Polynomial Regression and Expanded Hidden Markov model (eHMM) technique are applied to pre-processed OES data to detect endpoint.