Samsung Electronics Co., Ltd.

Samsung Sdi Co., Inc.

Sungkyunkwan University

Jiho Yoo

Younsuk Choi

Youngchun Kwon

Kyungdoc Kim

Sungjin Kim

Dongjin Lee

Jinwoo Park

Sanghyun Lee

Satoru Yamada

Ji Eun Lee

Han Jin Lim

Moonyoung Jeong

Sung Heo

Eunae Cho

Gyeongsu Park

Kyoung-Ho Jung

Yongsu Kim

Dong Su Ko

Dongseon Lee

Youngmin Nam

Seokho Kang

Hyun Uk Park

Hyosug Lee

Jaikwang Shin

Kyuyoung Hwang

Hyeonho Choi

Seungyeon Kwak

Sangho Park

Juhyun Kim

Hyun Soo Park

Wook Hwan Kim

Kum Hee Lee

Hyun Wook Park

Aram Jeon

Kyungin Choi

Whail Choi

Dmitry Kravchuk

Hasup Lee

Jun Chwae

Changho Noh

Haejun Yu

Minsik Park

Inkoo Kim

Ki-Hyung Ko

Dongmyoung Kim

Satoko Ishibe

Chanhee Lee

Misuk Kim

Soonwook Jung

Kyung Doc Kim

Sangheum Hwang

A semiconductor device and a method of manufacturing the same are provided. The semiconductor device includes a substrate including a trench. The semiconductor device further includes a gate electrode disposed in the trench, and a gate insulating film disposed between the substrate and the gate electrode. The gate electrode includes a gate conductor and a metal element, and an effective work function of the gate electrode is less than an effective work function of the gate conductor.

Semiconductor device and method of manufacturing the same

An organometallic compound represented by Formula 1:M(L)(L)  Formula 1

Organometallic compound, organic light-emitting device including the same, and diagnostic composition including the organometallic compound

A semiconductor device includes an active pattern provided on a substrate, a source/drain pattern provided on the active pattern, a channel pattern configured to be connected to the source/drain pattern, a gate electrode configured to be extended in a first direction and to cross the channel pattern, and a first spacer provided on a side surface of the gate electrode. The first spacer includes a fence portion provided on a side surface of the active pattern and below the source/drain pattern. The source/drain pattern includes a body portion and a neck portion between the body portion and the active pattern. The body portion includes a crystalline surface configured to be slantingly extended from the neck portion. The crystalline surface is configured to be spaced apart from an uppermost portion of the fence portion.

Semiconductor device and method of fabricating the same

An apparatus for optimizing experimental conditions by using a neural network may calculate a prediction yield and accuracy of the prediction yield by using a neural network-based experimental prediction model. The apparatus may optimize the experimental conditions by determining an experiment priority of a respective experiment condition combination based on the prediction yield and the prediction accuracy and receiving a feedback of results of experiments performed according to the experiment priority.

Apparatus and method of optimizing experimental conditions using neural network

Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Method and apparatus for generating chemical structure using neural network

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

Method and apparatus for generating a chemical structure using a neural network

A method of generating a molecular structure includes generating, based on a predetermined number of a plurality of nodes, all possible two-dimensional (2D) graphs and a plurality of edges representing connections between the plurality of nodes and, for each 2D graph from among the generated 2D graphs, generating all possible molecular structures based on the 2D graph by substituting each of the plurality of nodes with a polygonal ring structure including carbon atoms and substituting the edges with bonds between polygonal ring structures. Also, a method includes substituting at least one of carbon atoms included in the polygonal ring structures with an atom other than a carbon atom.

Apparatus and method for generating molecular structure

A method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.

Method and device for searching new material

A structure-generating method for generating a structure candidate of a new material including: by a structure-generating processor: performing machine learning on a machine learning model, wherein the machine learning model is configured to provide a result based on a descriptor of a material, a physical property of the material, and a structure of the material; and generating a structure candidate of the new material based on the result of the machine learning, wherein the new material has a target physical property, and wherein the descriptor of the material, the physical property of the material, and the structure of the material are stored in a database.

Method and apparatus for new material discovery using machine learning on targeted physical property

An organometallic compound represented by Formula 1: 

Organometallic compound and organic light-emitting device including the same

Growing community of inventors

Hwaseong-si, South Korea

Jiho Yoo