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수열합성법으로 성장된 ZnO 나노구조의 성장조건에 따른 특성
조민영,김민수,김군식,최현영,전수민,임광국,이동율,김진수,김종수,이주인,임재영,Cho, Min-Young,Kim, Min-Su,Kim, Ghun-Sik,Choi, Hyun-Young,Jeon, Su-Min,Yim, Kwang-Gug,Lee, Dong-Yul,Kim, Jin-Soo,Kim, Jong-Su,Lee, Joo-In,Leem, Jae-Young 한국재료학회 2010 한국재료학회지 Vol.20 No.5
ZnO nanostructures were grown on an Au seed layer by a hydrothermal method. The Au seed layer was deposited by ion sputter on a Si (100) substrate, and then the ZnO nanostructures were grown with different precursor concentrations ranging from 0.01 M to 0.3M at $150^{\circ}C$ and different growth temperatures ranging from $100^{\circ}C$ to $250^{\circ}C$ with 0.3 M of precursor concentration. FE-SEM (field-emission scanning electron microscopy), XRD (X-ray diffraction), and PL (photoluminescence) were carried out to investigate the structural and optical properties of the ZnO nanostructures. The different morphologies are shown with different growth conditions by FE-SEM images. The density of the ZnO nanostructures changed significantly as the growth conditions changed. The density increased as the precursor concentration increased. The ZnO nanostructures are barely grown at $100^{\circ}C$ and the ZnO nanostructure grown at $150^{\circ}C$ has the highest density. The XRD pattern shows the ZnO (100), ZnO (002), ZnO (101) peaks, which indicated the ZnO structure has a wurtzite structure. The higher intensity and lower FWHM (full width at half maximum) of the ZnO peaks were observed at a growth temperature of $150^{\circ}C$, which indicated higher crystal quality. A near band edge emission (NBE) and a deep level emission (DLE) were observed at the PL spectra and the intensity of the DLE increased as the density of the ZnO nanostructures increased.
수열합성법으로 성장된 ZnO 나노막대의 전구체 농도에 따른 구조적 및 광학적 특성
조민영,김민수,김군식,최현영,전수민,임광국,이동율,김진수,김종수,이주인,임재영,Cho, Min-Young,Kim, Min-Su,Kim, Ghun-Sik,Choi, Hyun-Young,Jeon, Su-Min,Yim, Kwang-Gug,Lee, Dong-Yul,Kim, Jin-Soo,Kim, Jong-Su,Lee, Joo-In,Leem, Jae-Young 한국진공학회 2010 Applied Science and Convergence Technology Vol.19 No.3
수열합성법을 이용하여 전구체 용액 농도에 따라 성장된 ZnO 나노막대의 특성에 대한 연구를 수행하였다. ZnO 씨앗층은 sol-gel법으로 코팅하였고, 그 위에 ZnO 나노막대는 전구체 용액 농도를 0.01 M에서 0.3 M로 변화하여 성장시켰다. FE-SEM (field-emission scanning electron microscopy), XRD (X-ray diffraction), PL (photoluminescence)을 사용하여 ZnO 나노막대의 특성 변화를 분석하였다. 전구체 용액의 농도가 증가함에 따라 ZnO 나노막대의 직경과 길이가 증가하였으며 광학적 특성이 향상되었다. ZnO nanorods were grown on ZnO seed layer by hydrothermal method. The ZnO seed layer was coated by sol-gel method, and then the ZnO nanorods on ZnO seed layer were grown with different precursor concentrations ranging from 0.01 M to 0.3 M. FE-SEM (field-emission scanning electron microscopy), XRD (X-ray diffraction), and PL (photoluminescence) were employed to investigate the structural and optical properties of the ZnO nanorods. The diameter and length of ZnO nanorods are increased and also the optical properties are enhanced as the precursor concentrations are increased.
갑상선 암의 림프절 전이를 모방한 이소성 갑상선 환자 1예
조민영,김동영 대한이비인후과학회 2023 대한이비인후과학회지 두경부외과학 Vol.66 No.6
Ectopic thyroid is a rare disease that occurs due to an error in the developmental stage of theembryo where the normal thyroid tissue is positioned at an area other than its normal site ofthe 2nd to 4th pretracheal area. Usually, the ectopic thyroid is discovered at the midline of apatient. We present this rare case of lateral ectopic thyroid mimicking the lymph node metas-tasis of thyroid cancer.
단일 클래스 분류기법을 이용한 반도체 공정 주기 신호의 이상분류
조민영,백준걸 대한산업공학회 2012 산업공학 Vol.25 No.2
Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.