• 한국진공학회지
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• 제16권4호
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• pp.273-278
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• 2007

양자폭포레이저에서 활성영역의 모의실험을 위하여 Runge-Kutta 방법과 shooting 방법을 이용하여 슈뢰딩거 방정식의 해를 구하였다. 활성영역의 두께 변화에 대하여 발진파장, 포논공명 에너지, 분극행렬요소 (dipole matrix element) 등의 특성변화를 관찰하였고, 이로부터 양자폭포레이저를 위한 에피성장에서 허용될 수 있는 최소한의 두께 정밀도를 제안하였다.

• 한국진공학회지
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• 제17권6호
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• pp.523-527
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• 2008

GaAs/AlGaAs 물질계를 기반으로 한 양자폭포레이저를 제작하였다. 양자폭포레이저는 활성층의 위까지만 식각된 shallow mesa 구조와 활성층까지 식각된 deep mesa 구조로 제작되었다. shallow mesa 구조의 경우 문턱전류 밀도가 $26-32\;kA/cm^2$이었지만 deep mesa 구조의 경우 문턱전류 밀도는 $13\;kA/cm^2$로 대단히 감소되었다. Deep mesa 구조에서의 문턱전류 감소는 측면 방향으로의 전류 손실이 감소되었기 때문으로 설명되었다.

• Applied Science and Convergence Technology
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• 제26권5호
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• pp.139-142
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• 2017

In this paper, InP-based InGaAs/InAlAs quantum cascade lasers(QCLs) providing nearly zero emission wavelength mismatch between the measured emission wavelength and the designed transition wavelength of QCLs is presented. The zero emission wavelength mismatch of QCLs influenced by both the accurate compositions and thicknesses of the low-pressure metal-organic vapor-phase epitaxy(MOVPE) grown InGaAs and InAlAs layers throughout the core and the abrupt composition transitions between InGaAs and InAlAs layers. The abrupt interfaces between InGaAs and InAlAs layers have been achieved throughout the core structure by means of controlling individually purged vent/run valves of a closed coupled showerhead reactor. In addition, maintaining substrate temperature constant during InGaAs/InAlAs core growth was a partial factor of uniformity improvement of QCLs. These approaches for reducing the possible discrepancies between the designed and MOVPE grown epitaxial structures could lead to improvement of QCL performance.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2006년도 제30회 학술논문발표회 초록집
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• pp.218-218
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• 2006

• 천문학회보
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• 제35권1호
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• pp.25-25
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• 2010

The spectroscopy over a region from 3 to 17 ${\mu}m$ based on the tuneable diode lasers (TDLAS) is the most powerful technique for in situ studies of the diagnostics of small molecules. The increasing interest in small molecules especially containing carbon, oxygen, hydrogen, and fluorine containing ones can be fulfilled by TDLAS at 0.0001 cm-1 resolution, because most of these compounds are infrared active. TDLAS provides a means of determining the absolute concentrations of the ground states of stable and transient molecular species, which can be employed for the time dependent studies in sub micro second scale. Information about gas temperature and population densities can also be derived from TDLAS measurements. Collisional energy transfer between the small molecules can be studied with TDLAS. Also, a variety of free radicals and molecular ions have been detected by TDLAS. Since plasmas with molecular feed gases are used in many applications, there are new applications in industrial field. Recently, the development of quantum cascade lasers (QCLs) offers an attractive new option for TDLAS.

• Current Optics and Photonics
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• 제6권1호
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.