Samsung Electronics Co., Ltd.

Jisu Ryu

Changwook Jeong

Min-Chul Park

Sanghoon Myung

Hyungtae Kim

Jinwoo Kim

Yohan Kim

Jinhyuk Choi

Yonghyun Park

Yongwoo Jeon

Sungmin Ahn

Yonghwan Hyun

Sengtai Lee

Jeonghoon Ko

Hyunjae Jang

Kanghyun Baek

Wonik Jang

Min Chul Jeon

Yunrong Li

Ami Ma

Daejin Kwak

According to certain embodiments, an electronic device comprises: a rechargeable battery configured to supply power to the electronic device; a processor connected to the battery; and memory storing a plurality of executable instructions, wherein the execution of the executable instructions by the processor causes performing a plurality of operations, wherein the plurality of operations comprises: charging the battery based on a target voltage level, wherein the target voltage level is based on a number of times the rechargeable battery has been previously charged, detecting that the voltage of the rechargeable battery is maintained within a range of levels lower than the target level, and protecting the battery in response to detecting.

Electronic device capable of identifying a state of a rechargeable battery

A method of generating a circuit model used to simulate an integrated circuit may include generating first feature element data and second feature element data by classifying feature data of a target semiconductor device according to measurement conditions, generating first target data and second target data by preprocessing the first feature element data and the second feature element data, respectively, generating a first machine learning model using the first target data and extracting a second machine learning model using the second target data, and generating the circuit model used to simulate the integrated circuit using the first machine learning model and the second machine learning model.

Methods of generating circuit models and manufacturing integrated circuits using the same

Various embodiments of the present invention relate to an electronic device for diagnosing a battery, and the electronic device may include a battery; and a power management module operatively connected with the battery, and including a charging circuit which controls charge of the battery, wherein the power management module is configured to monitor a charge state of the battery, if the battery reaches a first designated state, identify a time taken to change from the first designated state to a second designated state, and determine whether the battery is abnormal, based at least in part on the identified time. Other various embodiments are possible.

Electronic device and method for diagnosing battery thereof

According to an aspect of the present inventive concept, a simulation method for a semiconductor fabrication process includes obtaining, as input data, process parameters for controlling a semiconductor process of manufacturing semiconductor devices, or design parameters representing a structure of the semiconductor devices, or both the process parameters and the design parameters; generating predictive data for electrical characteristics of the semiconductor devices using a machine learning model based on the input data; generating reference data for the electrical characteristics of the semiconductor devices using a simulation tool based on the input data; and training the machine learning model using the predictive data and the reference data.

Simulation method for semiconductor fabrication process and method for manufacturing semiconductor device

A semiconductor wafer fault analysis system includes: a database to store a first reference map, which is classified as a first fault type, and a second reference map, which is classified as a second fault type; a first auto-encoder/decoder to remove a noise corresponding to the first fault type from the first reference map to generate a first pre-processed reference map; a second auto-encoder/decoder to remove a noise corresponding to the second fault type from the second reference map to generate a second pre-processed reference map; and a fault type analyzer. The database is updated based on the first and second pre-processed reference maps, and the fault type analyzer is to classify a fault type of a target map based on the updated database. The target map is generated by measuring a target wafer.

Semiconductor wafer fault analysis system and operation method thereof

A fault analysis method of a semiconductor fault analysis device is provided. The fault analysis method includes: receiving measurement data measured corresponding to a semiconductor device; generating double sampling data based on the measurement data and reference data; performing a fault analysis operation with respect to the double sampling data; classifying a fault type of the semiconductor device based on a result of the fault analysis operation; and outputting information about the fault type.

Semiconductor fault analysis device and fault analysis method thereof

A magnetic memory device can include an upper electrode, a lower electrode and a Magnetic Tunnel Junction (MTJ). The MTJ can include a reference magnetic pattern configured to generate a fixed magnetization and a free magnetic pattern on the reference magnetic pattern configured to generate a switchable magnetization that switches direction between parallel and anti-parallel to the fixed magnetization. A metal pattern can be on the free magnetic pattern and can be configured to conduct an in-plane current and a perpendicular-to-plane to/from the upper electrode.

Magnetic memory devices including in-plane current layers

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Hwaseong-si, South Korea

Jisu Ryu