Samsung Electronics Co., Ltd.

Seoul National University

Yongdeok Kim

Bonghyun Lee

Bongseok Lee

Heeran Lee

Namsu Ha

Munjun Seo

Sangho Lee

Sunghyun Kim

Gunhee Kim

Choonshik Leem

Kyung Geun Lee

Hyohyeong Kang

Injune Baek

Taelim Choi

Jaeho Han

Namyeong Kwon

Jinyoung Sung

Youngjae Yu

Jeong Yoon Eo

Ahn Jae Shin

Byung Gon Chun

Joonil Na

A method including identifying the operation mode of the electronic device as a first operation mode, sensing a change of setting corresponding to the first operation mode, determining whether to update setting information about the setting, based on at least one of a changing pattern or a change history of a user, and when the operation mode is identified later as the first operation mode, providing the user with the first operation mode with the plurality of updated settings including the setting information about the updated setting is provided.

Electronic device and method of setting operation mode thereof

A system with fault recovery includes: a plurality of worker nodes configured to perform distributed training; and a master node configured to control the plurality of worker nodes, wherein the master node is configured to: detect a fault of the plurality of worker nodes based on a predetermined period; adjust a collective communication participant list in response to the detecting of the fault; and transmit the adjusted participant list to one or more worker nodes in the adjusted participant list.

System, apparatus and method with fault recovery

Disclosed are a semiconductor device and a method of fabricating the same. The semiconductor device includes an area-oriented region and a performance-oriented region, standard cells disposed on each of the area-oriented region and the performance-oriented region, and a routing metal layer on the standard cells. The routing metal layer includes first routing lines on the area-oriented region and second routing lines on the performance-oriented region. The smallest line width of the first routing lines is a first width, the smallest line width of the second routing lines is a second width greater than the first width, a pitch between the first routing lines is a first pitch, and a pitch between the second routing lines is a second pitch greater than the first pitch.

Semiconductor device and method of fabricating the same

Provided are an electronic device and an operation method thereof. The electronic device includes a memory that stores one or more instructions, and a processor that executes the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to: divide original image data into a plurality of image sequences; determine a predetermined number of image sequences among the plurality of image sequences as an input image group, select one of the image sequences included in the input image group and add the selected image sequence to the highlight image group based on one or more image sequences pre-classified as a highlight image group, by using a trained model trained using an artificial intelligence algorithm; and generate summary image data extracted from the original image data, by using the image sequence included in the highlight image group.

Electronic device and operation method thereof

An electronic apparatus is provided. The electronic apparatus includes: a memory configured to store volume histories for a plurality of content sources; and a processor configured to, based on a content source for providing a content being changed from a first content source to a second content source, among the plurality of content sources, identify a volume level corresponding to the second content source based on a volume history of the first content source and a volume history of the second content source among the stored volume histories, and change a currently-set volume level to the identified volume level, and update the volume history of the second content source based on the changed volume level.

Electronic apparatus and controlling method thereof

A non-transitory computer-readable medium for monitoring a semiconductor fabrication process includes an image conversion model having an artificial neural network. The image conversion model, when executed, causes the processor to receive a first image and a second image of a semiconductor wafer. The artificial neural network is trained by inputting a dataset representing the first image and the second image, generating a conversion image of the semiconductor wafer and calibrating weights and biases of the artificial neural network to match the conversion image to the second image. A third image of the semiconductor wafer is generated based on the calibrated weights and biases of the artificial neural network. The image conversion model with the trained artificial neural network may be transmitted to another device for image conversion of low resolution images.

Non-transitory computer-readable medium and method for monitoring a semiconductor fabrication process

A semiconductor defect classification device includes feature extractors that are configured to receive images of semiconductor patterns on a wafer and to extract features of the images from the images, and a classifier that is configured to receive the features of the images and first meta information about the wafer and to use machine learning to classify a defect of the semiconductor patterns associated with the images based on the features of the images and the first meta information.

Semiconductor defect classification device, method for classifying defect of semiconductor, and semiconductor defect classification system

Growing community of inventors

Hwaseong-si, South Korea

Yongdeok Kim