Abstract: Disclosed is a substrate treating apparatus. The substrate treating apparatus includes an imaging unit that photographs loci of the one or more discharge liquids discharged from the plurality of nozzles, and an inspection unit that calculates impact points of the one or more discharge liquids discharged from the plurality of nozzles and determines whether the impact points of the one or more discharge liquids discharged from the plurality of nozzles are normal. The inspection unit includes an image synthesizing unit that synthesizes a plurality of images captured by the imaging unit, a pre-processing unit that pre-processes image data generated through the image synthesizing unit, and a calculation unit that calculates whether the impact points of the one or more discharge liquids discharged from the plurality of nozzles are normal by comparing the image data pre-processed by the pre-processing unit.

Abstract: A semiconductor device includes: an active pattern extending in a first direction across an underlying substrate, a gate structure extending in a second direction, on the active pattern, a first source/drain contact electrically connected to a source/drain region within the active pattern, on one side of the gate structure, and a first via pattern electrically connected to an upper surface of the first source/drain contact. A rail pattern is provided, which extends in the first direction, and is spaced apart from the first via pattern in the second direction. A wiring pattern extends in the first direction, and is electrically connected to an upper surface of the rail pattern. The first source/drain contact includes a first recess therein, which is more recessed downwardly relative to the upper surface of the first source/drain contact, and at least a portion of the first recess extends adjacent to the rail pattern.

Abstract: Disclosed are an ultra-high sensitivity target signal detection method based on analysis of a noise signal of a sensor using deep learning based anomaly detection and a system using the same. More particularly, disclosed are a method and apparatus for receiving a noise signal from a sensor and inputting data to an artificial neural network trained with a normal noise signal to determine whether or not a target signal is present. The target signal detection method is capable of detecting a target signal having a very low concentration that can be detected by a conventional sensor, whereby the target signal detection method is useful in developing an ultra-high sensitivity sensor.

Abstract: Disclosed are an ultra-high sensitivity target signal detection method based on analysis of a noise signal of a sensor using deep learning based anomaly detection and a system using the same. More particularly, disclosed are a method and apparatus for receiving a noise signal from a sensor and inputting data to an artificial neural network trained with a normal noise signal to determine whether or not a target signal is present. The target signal detection method is capable of detecting a target signal having a very low concentration that can be detected by a conventional sensor, whereby the target signal detection method is useful in developing an ultra-high sensitivity sensor.

Abstract: The present disclosure relates to a sensor including a nanostructure and a method for manufacturing the same.