Abstract: A semiconductor wafer cleaning composition for used in a semiconductor device manufacturing process and a method of cleaning a semiconductor wafer using the cleaning composition are provided. The cleaning composition includes surfactants represented by Formula 1 and Formula 2, respectively, an organic or inorganic acid, and water occupying for the remaining proportion. The cleaning method is a method of immersing a semiconductor wafer in the cleaning composition for 100 to 500 seconds. The cleaning composition and the cleaning method according to the present disclosure provide an incredibility improved removal rate and an effective cleaning power for contaminants, especially organic wax, during a process of polishing the surface of a wafer used to manufacture semiconductor devices, thereby providing a super-cleaned wafer surface, resulting in production of reliable semiconductor devices.

Abstract: A depth sensor includes a substrate that includes a first face and a second face opposite to each other in a first direction; a photoelectric conversion element disposed in the substrate; and first and second taps connected to the photoelectric conversion element. Each of the first and second taps includes: a floating diffusion area disposed in the substrate; a transfer transistor connected to the floating diffusion area; a photo transistor connected to the photoelectric conversion element; a tap transfer transistor connected to the photo transistor; and a storage transistor connected to the tap transfer transistor and the transfer transistor. The storage transistor includes a storage gate electrode. The storage gate electrode includes a first extension and a second extension that extend from the first face of the substrate toward the second face, and the first extension and the second extension are spaced apart from each other in a second direction.

Abstract: A display device includes a substrate including pixels including a first pixel and a second pixel, a driver of the first pixel disposed on the substrate, a driver of the second pixel disposed on the substrate, a first pixel electrode overlapping the driver of the first pixel and electrically connected to the driver of the first pixel, a second pixel electrode overlapping the driver of the first pixel and the driver of the second pixel and electrically connected to the driver of the second pixel, an emission layer disposed on the first pixel electrode and the second pixel electrode, and a common electrode disposed on the emission layer, wherein the second pixel electrode includes a first opening overlapping the driver of the first pixel.

Abstract: An automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application includes receiving one or more pieces of data among vibration data, noise data, and controller area network (CAN) data, a data input processing operation of trimming the input data, an operation of extracting features from the trimmed data, setting a setting value of a hyper-parameter with respect to the one or more pieces of data thereamong, and generating a total of N models to include both of machine learning (ML) and deep learning (DL) as N individual models and generating ensemble prediction model structures for the N individual models. As a parameter updating is being proceeded due to the hyper-parameter so as to minimize values of cost functions of the N individual models, a reward for model accuracy performance is optimized and the ensemble prediction model structures of the N individual models change.

Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus includes receiving at least some of omics levels that make up omics through a user interface, receiving at least some types of mutual association degrees among a plurality of types of mutual association degrees, selecting a DB for the at least some of the omics levels and a DB for the at least some types of mutual association from an omics DB including data for each omics level and data for each type of mutual association, generating a first matrix composed of the DB for the at least some of the omics levels and the DB for the at least some types of mutual association degrees, receiving a predetermined search word through the user interface, extracting a plurality of biological entities, and generating a multi-omics network in which a plurality of nodes.

Abstract: A method includes generating a DB matrix composed of a selected biological entity and a selected type of mutual association degree from an omics DB, receiving a search word, extracting biological entities, extracting a degree of mutual association between the search word and the biological entities from the DB matrix, generating a first knowledge network in which the search word and each of the biological entities are used as nodes and a plurality of nodes are connected using a connection line according to a degree of mutual association between the search word and the biological entities or a degree of mutual association between the biological entities, computing a graph theory index for each of the plurality of nodes of the first knowledge network, and generating a second knowledge network using some nodes selected using the graph theory index among the plurality of nodes of the first knowledge network.

Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus, includes receiving a predetermined search word, extracting at least one biological entity related to the predetermined search word from a big data database (DB), extracting a degree of mutual association between the predetermined search word and the at least one biological entity, generating a first knowledge network in which a plurality of nodes including the predetermined search word and the at least one biological entity are connected according to the degree of mutual association, computing a graph theory index of the first knowledge network, and generating a second knowledge network using some nodes of the plurality of nodes of which the graph theory index is equal to or greater than a threshold value.

Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus includes receiving at least some of omics levels that make up omics through a user interface, receiving at least some types of mutual association degrees among a plurality of types of mutual association degrees, selecting a DB for the at least some of the omics levels and a DB for the at least some types of mutual association from an omics DB including data for each omics level and data for each type of mutual association, generating a first matrix composed of the DB for the at least some of the omics levels and the DB for the at least some types of mutual association degrees, receiving a predetermined search word through the user interface, extracting a plurality of biological entities, and generating a multi-omics network in which a plurality of nodes.

Abstract: A method for deriving a new drug candidate substance that is executed by a computing apparatus is disclosed. The method includes generating a refined knowledge network in which nodes representing biological entities are connected to each other by using a connecting line representing a correlation between the nodes, determining a basic drug for deriving a new drug candidate substance by analyzing drug-disease node pairs existing in the refined knowledge network, and obtaining an analogous substance having a chemical structure analogous to a structure of the basic drug by using an artificial neural network-based structure prediction model. The biological entity includes at least one of a gene, a protein, a metabolite, a symptom, a disease, a compound, and a drug, and a simplified molecular-input line-entry system (SMILES) based character string of the basic drug is input in the structure prediction model.

Abstract: A data processing method for discovering a new drug candidate substance by a data processing apparatus according to an embodiment of the present invention includes receiving a predetermined search word through a user interface unit, extracting a plurality of druggable paths related to the predetermined search word and a druggable path (DP) index for each druggable path by using an artificial neural network (ANN) model, selecting some of the druggable paths having a relatively high DP index among the plurality of druggable paths, extracting information on absorption, distribution, metabolism, excretion, and toxicity (ADMET information) for the some of the druggable paths by using an ADMET model, and outputting the DP index and the ADMET information for each of the some of the druggable paths.