Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Cheon Lee

Tammy Lee

Donghyun Kim

Yong Woo Lee

Mun Churl Kim

Sungho Kang

Youngsu Moon

Iljun Ahn

Jae Seok Choi

Jaeyeon Park

Hyunseung Lee

Yongsup Park

Doug Yong Sung

Yunjae Lim

Taegyoung Ahn

Seung Bo Shim

Yongwoo Kim

Young Jin Noh

Hyungdal Kwon

Masato Horiguchi

Ji Soo Im

Peter Byung H Han

Hyeong Mo Kang

Plasma processing equipment includes a chuck stage for supporting a wafer and including a lower electrode, an upper electrode disposed on the chuck stage, an AC power supply which applies first to third signals having different magnitudes of frequencies to the upper electrode or the lower electrode, a dielectric ring which surrounds the chuck stage, an edge electrode located within the dielectric ring, and a resonance circuit connected to the edge electrode. The resonance circuit includes a filter circuit which allows only the third signal among the first to third signals to pass, and a series resonance circuit connected in series with the filter circuit and having a first coil and a first variable capacitor connected in series and grounded.

Plasma processing equipment

The present disclosure relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm, and applications therefore. Disclosed is an electronic device. The electronic device comprises: a storage unit which stores therein an artificial intelligence model trained to determine parameters for a plurality of filters used for image processing on the basis of a deep neural network (DNN); and a processor for determining, through the artificial intelligence mode, parameters for each of the plurality of filters used for image processing for an input image, and performing, through the plurality of filters, filtering of the input image on the basis of the determined parameters so as to perform image processing for the input image.

Electronic device, image processing method of electronic device, and computer-readable medium

An encoding apparatus connected to a learning circuit processing learning of a deep neural network and configured to perform encoding for reconfiguring connection or disconnection of a plurality of edges in a layer of the deep neural network using an edge sequence generated based on a random number sequence and dropout information indicating a ratio between connected edges and disconnected edges of a plurality of edges included in a layer of the deep neural network.

Method of performing learning of deep neural network and apparatus thereof

Growing community of inventors

Suwon-si, South Korea

Cheon Lee