• Journal of the Korean Vacuum Society
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• v.16 no.4
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• pp.273-278
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• 2007

For the simulation of active region in the quantum cascade lasers (QCL), we solved Schrodinger equation utilizing Runge-Kutta method and Shotting method. Wavelength, phonon resonant energy, and dipole matrix element were simulated with the variation of active region thickness. As a result of such simulation, it was suggested the tolerance range of epi-layer thickness error when 3-quantum well QCL structures are grown.

• Journal of the Korean Vacuum Society
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• v.17 no.6
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• pp.523-527
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• 2008

GaAs/AlGaAs based quantum cascade lasers were fabricated with two different types of i) the shallow mesa type which was etched up to above active region and ii) the deep mesa type which was etched through active region. While the threshold current density of shallow mesa type was $26-32\;kA/cm^2$, the one of deep mesa type was reduced drastically up to $13\;kA/cm^2$. Such lowered threshold current density at deep mesa type attributed to the reduction of current loss to the lateral directions.

• Applied Science and Convergence Technology
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• v.26 no.5
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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.

• Proceedings of the Korean Vacuum Society Conference
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• 2006.02a
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• pp.218-218
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• 2006

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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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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• v.6 no.1
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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.