公开(公告)号：US12182486B2

公开(公告)日：2024-12-31

申请号：US17491739

申请日：2021-10-01

Applicant: Samsung Electronics Co., Ltd.

Inventor： Sangwoon Lee ,  Joohyun Jeon ,  Sungjin Kim ,  Seunghyun Kim ,  Wonki Roh ,  Chulwoo Park ,  Seongjae Byeon ,  Taeyoon An ,  Hyoeun Jung

IPC: G06F30/3308 ,  G06F30/25

Abstract: A method of modeling damages to a crystal caused by an incident particle includes obtaining particle information and crystal information; estimating energy loss of the incident particle based on the particle information and the crystal information; estimating a volume of a vacancy based on the energy loss; estimating a vacancy reaction based on the crystal information and the volume of the vacancy; and generating output data based on the vacancy reaction, the output data including quantification data of the damages.

公开(公告)号：US11765885B2

公开(公告)日：2023-09-19

申请号：US17210931

申请日：2021-03-24

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Hyewon Kim ,  Juhyung We ,  Sungmi Yoon ,  Donghyun Im ,  Sangwoon Lee ,  Taiuk Rim ,  Kyosuk Chae

IPC: H10B12/00 ,  H01L21/28 ,  H01L29/423 ,  H01L29/10 ,  H01L29/78

CPC classification number: H10B12/34 ,  H01L21/28044 ,  H01L29/1037 ,  H01L29/4236 ,  H01L29/7834 ,  H10B12/053 ,  H10B12/315

Abstract: A semiconductor device including a substrate including a recess; a gate insulation layer on a surface of the recess; a first gate pattern on the gate insulation layer and filling a lower portion of the recess; a second gate pattern on the first gate pattern in the recess and including a material having a work function different from a work function of the first gate pattern; a capping insulation pattern on the second gate pattern and filling an upper portion of the recess; a leakage blocking oxide layer on the gate insulation layer at an upper sidewall of the recess above an upper surface of the first gate pattern and contacting a sidewall of the capping insulation pattern; and impurity regions in the substrate and adjacent to the upper sidewall of the recess, each impurity region having a lower surface higher than the upper surface of the first gate pattern.

公开(公告)号：US11824081B2

公开(公告)日：2023-11-21

申请号：US17209762

申请日：2021-03-23

Applicant: Samsung Electronics Co., Ltd. ,  AJOU UNIVERSITY INDUSTRY-ACADEMIC COOPERATION FOUNDATION

Inventor： Changsoo Lee ,  Sangwoon Lee ,  Chan Kwak ,  Hyungjun Kim ,  Euncheol Do

IPC: H01L49/02 ,  H01L27/108 ,  C04B35/057 ,  C04B35/01 ,  C04B35/64 ,  C04B35/495 ,  H10B12/00

CPC classification number: H01L28/55 ,  C04B35/01 ,  C04B35/057 ,  C04B35/495 ,  C04B35/64 ,  H10B12/038 ,  H10B12/37

Abstract: Provided are a dielectric thin film, an integrated device including the same, and a method of manufacturing the dielectric thin film. The dielectric thin film includes an oxide having a perovskite-type crystal structure represented by Formula 1 below and wherein the dielectric thin film comprises 0.3 at % or less of halogen ions or sulfur ions.

A2-xB3-yO10-z  

In Formula 1, A, B, x, y, and z are disclosed in the specification.

公开(公告)号：US20220268830A1

公开(公告)日：2022-08-25

申请号：US17495487

申请日：2021-10-06

Applicant: Samsung Electronics Co., Ltd.

Inventor： Taeyoon An ,  Sangwoon Lee ,  Sungjin Kim ,  Seunghyun Kim ,  Wonki Roh ,  Chulwoo Park ,  Seongjae Byeon ,  Joohyun Jeon ,  Hyoeun Jung

IPC: G01R31/28 ,  G06F30/20 ,  G06N20/00 ,  G01T1/24

Abstract: A method for predicting a defect in a semiconductor device includes: calculating a first probability that particles will be generated in a semiconductor element by radiation; calculating a second probability that damage will occur in the semiconductor element due to the particles; generating a training data set using input data and simulation data, the input data including damage data generated using the first probability and the second probability and including at least one of a position in which the damage will occur and an amount of the damage, impurity concentration of impurities doped in at least a portion of the semiconductor element, and structural data of the semiconductor element, and the simulation data including electrical characteristics of the semiconductor element obtained as a result of a simulation based on the input data; and training a machine learning model based on the training data set to generate a defect prediction model.