Abstract: A semiconductor device may include an active pattern on a substrate; an isolation pattern on the substrate, the isolation pattern covering opposite sidewalls of the active pattern; a liner on the isolation pattern, a liner including a material different from the isolation pattern; a gate structure contacting an upper surface of the active pattern and an upper surface of the liner; and a plurality of channels spaced apart from each other in a vertical direction perpendicular to an upper surface of the substrate, each of the plurality of channels extending through the gate structure.

Abstract: Disclosed is a an in-cell type display device including a touch screen function which is capable of preventing a luminance from being changed in accordance with a viewing angle, and omitting a process of connecting touch electrodes provided in an upper substrate with Tx driver and Rx driver connected with a lower substrate, wherein the display device includes a lower substrate, a thin film transistor layer including thin film transistors provided on the lower substrate, an encapsulation layer provided on the thin film transistor layer, color filters provided on the encapsulation layer, a black matrix provided between the color filters and provided on the encapsulation layer, and first touch electrodes overlapped with the black matrix.

Abstract: A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image

Abstract: Disclosed are a radar image generation method and an apparatus for performing the same. The radar image generation method includes receiving a received signal received at each of radars that are distributed and arranged; generating an input signal by processing the received signal; generating a support vector based on the input signal; updating the support vector; updating a coefficient corresponding to the support vector; and generating a radar image based on the support vector and the coefficient.

Abstract: A semiconductor device and a method of manufacturing the same are provided. The semiconductor device includes a substrate including a trench. The semiconductor device further includes a gate electrode disposed in the trench, and a gate insulating film disposed between the substrate and the gate electrode. The gate electrode includes a gate conductor and a metal element, and an effective work function of the gate electrode is less than an effective work function of the gate conductor.

Abstract: Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Abstract: A semiconductor device and a method of manufacturing the same are provided. The semiconductor device includes a substrate including a trench. The semiconductor device further includes a gate electrode disposed in the trench, and a gate insulating film disposed between the substrate and the gate electrode. The gate electrode includes a gate conductor and a metal element, and an effective work function of the gate electrode is less than an effective work function of the gate conductor.

Abstract: An apparatus for optimizing experimental conditions by using a neural network may calculate a prediction yield and accuracy of the prediction yield by using a neural network-based experimental prediction model. The apparatus may optimize the experimental conditions by determining an experiment priority of a respective experiment condition combination based on the prediction yield and the prediction accuracy and receiving a feedback of results of experiments performed according to the experiment priority.

Abstract: Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Abstract: A semiconductor device includes an active pattern provided on a substrate, a source/drain pattern provided on the active pattern, a channel pattern configured to be connected to the source/drain pattern, a gate electrode configured to be extended in a first direction and to cross the channel pattern, and a first spacer provided on a side surface of the gate electrode. The first spacer includes a fence portion provided on a side surface of the active pattern and below the source/drain pattern. The source/drain pattern includes a body portion and a neck portion between the body portion and the active pattern. The body portion includes a crystalline surface configured to be slantingly extended from the neck portion. The crystalline surface is configured to be spaced apart from an uppermost portion of the fence portion.

Abstract: A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

Abstract: A method of generating a molecular structure includes generating, based on a predetermined number of a plurality of nodes, all possible two-dimensional (2D) graphs and a plurality of edges representing connections between the plurality of nodes and, for each 2D graph from among the generated 2D graphs, generating all possible molecular structures based on the 2D graph by substituting each of the plurality of nodes with a polygonal ring structure including carbon atoms and substituting the edges with bonds between polygonal ring structures. Also, a method includes substituting at least one of carbon atoms included in the polygonal ring structures with an atom other than a carbon atom.

Abstract: A semiconductor device and a method of manufacturing the same are provided. The semiconductor device includes a substrate including a trench. The semiconductor device further includes a gate electrode disposed in the trench, and a gate insulating film disposed between the substrate and the gate electrode. The gate electrode includes a gate conductor and a metal element, and an effective work function of the gate electrode is less than an effective work function of the gate conductor.

Abstract: A semiconductor device and a method of manufacturing the same are provided. The semiconductor device includes a substrate including a trench. The semiconductor device further includes a gate electrode disposed in the trench, and a gate insulating film disposed between the substrate and the gate electrode. The gate electrode includes a gate conductor and a metal element, and an effective work function of the gate electrode is less than an effective work function of the gate conductor.

Abstract: A method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.

Abstract: A neural network apparatus for generating a new chemical structure may receive a structure input of a chemical structure; generate, based on the structure input, a negative attention vector that indicates a respective probability of presence of each of a plurality of blacklists in the structure input; generate a structure expression by encoding the structure input; generate a final reverse blacklist vector that does not include the plurality of blacklists, based on the negative attention vector and the structure expression; and generate the new chemical structure by decoding the final reverse blacklist vector.

Abstract: A method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.

Abstract: An apparatus for optimizing experimental conditions by using a neural network may calculate a prediction yield and accuracy of the prediction yield by using a neural network-based experimental prediction model. The apparatus may optimize the experimental conditions by determining an experiment priority of a respective experiment condition combination based on the prediction yield and the prediction accuracy and receiving a feedback of results of experiments performed according to the experiment priority.

Abstract: A structure-generating method for generating a structure candidate of a new material including: by a structure-generating processor: performing machine learning on a machine learning model, wherein the machine learning model is configured to provide a result based on a descriptor of a material, a physical property of the material, and a structure of the material; and generating a structure candidate of the new material based on the result of the machine learning, wherein the new material has a target physical property, and wherein the descriptor of the material, the physical property of the material, and the structure of the material are stored in a database.

Abstract: An organometallic compound represented by Formula 1: wherein, in Formula 1, groups and variables are the same as described in the specification.