Abstract: Provided is a Raman scattering measurement apparatus including a light source which emits light to smoke particles, a filter configured to block light which is incident to the smoke particles and passes through the particle and to allow Raman scattered light to pass therethrough, and a photodetector which detects the Raman scattered light passing through the filter in order to distinguish fire smoke generated due to a true fire from non-fire smoke generated due to daily life or industrial activity. The present invention also provides a fire determination apparatus including a unit which reads a Raman shift from Raman scattered light detected by the photodetector of the Raman scattering measurement apparatus, estimates a smoke component from the read Raman shift, and determines fire/non-fire from the estimated smoke component and a method thereof.

Abstract: A deep learning-based stent prediction method including: setting as a region of interest, a region in which a procedure is to be performed among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest, obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals, extracting feature information about procedure information of the target patient, obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images, and predicting progress of a stent procedure containing a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.

Abstract: A method of analyzing a plaque tissue component based on deep learning, the method including: extracting a plurality of first intravascular ultrasound (IVUS) cross-sectional images into which a first IVUS image that is a preprocedural IVUS image of a patient is divided at predetermined intervals; labeling each of the plurality of first IVUS cross-sectional images by using label indices corresponding to plaque tissue components to form labeled images, performing image conversion to obtain a polar coordinate image through which a distribution of tissue components for each angle is identifiable by performing a coordinate transformation based on the labeled images, extracting a label vector for each angle based on the polar coordinate image, and outputting output data obtained by quantifying the tissue components for each angle by using an artificial intelligence model that is trained by using, as training data, the label vector for each angle.

Abstract: A camera module includes a housing, a movable body configured to move in a direction of an optical axis of the housing; a reinforcing member formed integrally on one surface of the movable body, and configured to increase a rigidity of the movable body; and a first buffer member formed in the reinforcing member, and configured to reduce an impactive force between the housing and the movable body.

Abstract: A method of manufacturing a semiconductor device comprising: obtaining a raw light signal by selecting a predetermined wavelength band of light reflected from a wafer on which a plurality of patterns are formed; converting the raw light signal into a frequency domain; obtaining a first detection signal having a first frequency band from the raw light signal converted into the frequency domain; obtaining a second detection signal having a second frequency band from the raw light signal converted into the frequency domain, the second frequency band being different from the first frequency band; obtaining a representative value using the first detection signal, the representative value representing a profile of the plurality of patterns; and obtaining a distribution value using the second detection signal, the distribution value representing a profile of the plurality of patterns using the second detection signal.

Abstract: A method of manufacturing a semiconductor device comprising: obtaining a raw light signal by selecting a predetermined wavelength band of light reflected from a wafer on which a plurality of patterns are formed; converting the raw light signal into a frequency domain; obtaining a first detection signal having a first frequency band from the raw light signal converted into the frequency domain; obtaining a second detection signal having a second frequency band from the raw light signal converted into the frequency domain, the second frequency band being different from the first frequency band; obtaining a representative value using the first detection signal, the representative value representing a profile of the plurality of patterns; and obtaining a distribution value using the second detection signal, the distribution value representing a profile of the plurality of patterns using the second detection signal.

Abstract: A 3D profiling system of a semiconductor chip is provided and includes a storage unit that receives scanning electron microscope (SEM) images of a plurality of semiconductor devices having respective data with respect to a plurality of different components and gray levels of each SEM image. An extraction unit that performs principal component analysis (PCA) on the gray level of the SEM image and separates principal components from among the plurality of different components is also part of the system. Additionally, a calculation unit receives provision of actually measured values of the plurality of semiconductor devices, and applies a multiple linear regression to the principal components based on the measured values to complete a 3D profile of the semiconductor chip.

Abstract: A 3D profiling system of a semiconductor chip is provided and includes a storage unit that receives scanning electron microscope (SEM) images of a plurality of semiconductor devices having respective data with respect to a plurality of different components and gray levels of each SEM image. An extraction unit that performs principal component analysis (PCA) on the gray level of the SEM image and separates principal components from among the plurality of different components is also part of the system. Additionally, a calculation unit receives provision of actually measured values of the plurality of semiconductor devices, and applies a multiple linear regression to the principal components based on the measured values to complete a 3D profile of the semiconductor chip.