Samsung Electronics Co., Ltd.

Minseok Choi

Kwangpyo Choi

Jaehwan Kim

Youngo Park

Jongseok Lee

Quockhanh Dinh

Sunyoung Jeon

Jeonggil Kim

Jaemin Lee

Donghyun Lee

Seonju Lee

Jeonghyun Lee

Dongyeong Kim

Byoungmok Kim

Yang-yeol Gu

Yountae Shin

Chomin Lee

Ae-ryun Kim

Oungu Lee

Sunghyup Kim

Ho Jun Yu

Jongbin Park

Inho Choi

Jaehyun Kwon

Sung Hwan Lee

Gilwoo Song

Jinwoo Nam

Heeseung Shin

Sungyeol Kim

Pei Zheng

Injae Lee

Eunhee Jeang

Wondon Joo

Hyung Joo Kim

Jinhwan Lee

Yoojin Jeong

Donghyub Lee

Dohyun Jung

Moonhyun Jung

Chin-Jung Liu

Matsuda Yozo

An extreme ultraviolet (EUV) collector inspection apparatus includes a light blocking cover covering a front surface of an EUV collector to be inspected and providing a space portion in which external light is blocked, a light source in the space portion, the light source having a pillar shape extending along a central axis of the EUV collector, the light source configured to output irradiated light ranging from an ultraviolet (UV) band to a visible light (VIS) band, and a spectrometer above the light source and configured to detect a spectrum of reflected light from the irradiated light reflected from the front surface of the EUV collector. The apparatus or a controller associated therewith may be configured to inspect a contamination state of the front surface of the EUV collector based on the spectrum of reflected light.

EUV collector inspection apparatus and inspection method

A refrigerator including: a main body forming a storage compartment and a door provided to open and close the storage compartment, wherein the door includes a door frame, a cover arranged in front of the door frame and including a cover fixer, and a door panel arranged in front of the cover, the door panel including a first fixer and a second fixer on a rear surface of the door panel. When the door panel is coupled to a front side of the cover, the first fixer is coupled to the door frame and the second fixer is coupled to the cover.

Refrigerator

A server and an electronic device for identifying a fake image are provided. The server includes a memory storing an artificial intelligence model trained to identify a fake image; and a processor connected to the memory, and configured to identify whether an image is the fake image by inputting the image to the artificial intelligence model, wherein the artificial intelligence model is a model trained based on an original image and a sample fake image, each including information about a landmark of a face area.

Server, electronic device, and control methods therefor

A target debris collection device for extreme ultraviolet (EUV) light source apparatus, includes a baffle body extending within an EUV vessel between a collector and an outlet port of the EUV vessel to allow EUV light reflected from the collector to pass through an internal transmissive region thereof, a discharge plate provided in a first end portion of the baffle body adjacent to the collector to collect the target material debris on an inner surface of the baffle body, a guide structure to guide the target material debris collected in the discharge plate to a collection tank, and a first heating member provided in the guide structure to prevent the target material debris from being solidified.

Target debris collection device and extreme ultraviolet light source apparatus including the same

A controlling method of an electronic apparatus may include: obtaining image data and metadata regarding the image data, the image data comprising a first image frame and a second image frame that is subsequent to the first image frame; obtaining information regarding a region of interest of the first image frame by inputting the first image frame to a first neural network model; obtaining a similarity between the first image frame and the second image frame based on motion vector information included in the metadata; and detecting whether there is a manipulated area in the second image frame based on the similarity.

Electronic apparatus and controlling method thereof

An apparatus for removing a residue of an EUV light source vessel including an internal side surface having a curved surface is provided. The apparatus includes a frame portion configured to be disposed on a bottom surface of an EUV light source vessel and a head portion above the frame portion. The head portion is configured to be rotatably moved on a circular trajectory while maintaining a desired (and/or alternatively predetermined) distance from the curved surface of the EUV light source vessel. The head portion may have a heating member configured to emit heat toward the curved surface of the EUV light source vessel. The heating member may have a shape curved in an arc corresponding to a portion of the circular trajectory.

Apparatus for removing residue of EUV light source vessel

An artificial intelligence (AI) decoding apparatus includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions to obtain image data corresponding to a first image that is downscaled from an original image by using first parameters of a first filter kernel included in a first deep neural network (DNN), reconstruct a second image corresponding to the first image, based on the obtained image data, and obtain a third image that is upscaled from the reconstructed second image, by performing an operation between the reconstructed second image and second parameters of a second filter kernel included in a second DNN corresponding to the first DNN. Each of the second parameters is represented by a product of a scale factor and one among integer values, and each of the integer values is 0 or &#xb1;2, where n is an integer.

Artificial intelligence (AI) encoding device and operating method thereof and AI decoding device and operating method thereof

A droplet accelerating assembly includes an acceleration chamber extending in a first direction parallel to an ejection direction of the droplet, the acceleration chamber having a first side connected to the droplet generator, a second side opposite the first side in the first direction, the second side including a discharge hole, and a fluid flow path, a pressure controller connected to the fluid flow path of the acceleration chamber, the pressure controller being configured to adjust an internal pressure of the acceleration chamber, an electrifier in the acceleration chamber, the electrifier being configured to electrify the droplet ejected by the droplet generator into an electrified droplet, and an accelerator in the acceleration chamber, the accelerator being configured to accelerate the electrified droplet.

Droplet accelerating assembly and extreme ultra-violet lithography apparatus including the same

An artificial intelligence (AI) decoding apparatus includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions, to obtain image data corresponding to a first image that is encoded, obtain a second image corresponding to the first image by decoding the obtained image data, determine whether to perform AI up-scaling of the obtained second image, based on the AI up-scaling of the obtained second image being determined to be performed, obtain a third image by performing the AI up-scaling of the obtained second image through an up-scaling deep neural network (DNN), and output the obtained third image, and based on the AI up-scaling of the obtained second image being determined to be not performed, output the obtained second image.

Apparatuses and methods for performing artificial intelligence encoding and artificial intelligence decoding on image

Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to obtain the first image from the original image.

Methods and apparatuses for performing artificial intelligence encoding and artificial intelligence decoding on image

Growing community of inventors

Suwon-si, South Korea

Minseok Choi