Samsung Electronics Co., Ltd.

Hyundai Motor Company

Kia Corporation

Ace Antenna Corporation

Seyoung Park

Jonghyun Lee

Gwangnae Gil

Sola Woo

Hongsik Kim

Jihoon Sung

Jungnam Bae

Byoung Hyun Lee

Shigenobu Maeda

Wook Rae Kim

Sangpil Kang

Heejun Kim

Minhong Yun

Daehan Han

Seongjin Yoo

A method of operating an electrical test prediction apparatus includes determining a relationship between first electrical test (ET) data, corresponding to at least one shot region comprising a subset of a plurality of semiconductor chips of a wafer, and electrical die sorting (EDS) data, obtained by measuring a state of each chip on the wafer by a testing device, and predicting second ET data, corresponding to an region of the wafer other than the at least one shot region by performing machine learning on the relationship.

Computing devices for predicting electrical tests, electrical test prediction apparatuses having the same, and operating methods thereof

To predict characteristics of a semiconductor device, a simulation current-voltage curve of the semiconductor device is generated using compact models where each compact model generates simulation result data by performing a simulation based on device data. The simulation result data indicate characteristics of semiconductor devices corresponding to the device data. The compact models respectively corresponding to process data and semiconductor products. Simulation reference points on the simulation current-voltage curve are extracted. Basic training data corresponding to a combination of the simulation reference points and the simulation current-voltage curve are generated. A deep learning model is trained based on the basic training data such that the deep learning model outputs a prediction current-voltage curve. A target prediction current-voltage curve is generated based on the deep learning model and target reference points corresponding to the target semiconductor product. The deep learning model is a generative adversarial network.

Method of predicting characteristic of semiconductor device and computing device performing the same

Provided is a semiconductor process modeling system. The semiconductor process modeling system includes a preprocessing component configured to generate tensor data from raw data obtained from semiconductor manufacturing equipment, wherein, when the raw data is expressed as a raw matrix representing values of a plurality of process parameters for each of a plurality of wafers, at least one element of the raw matrix is omitted, when the tensor data is expressed as a tensor matrix representing values of a plurality of preprocessed process parameters for each of the plurality of wafers, the number of omitted elements of the tensor matrix is less than the number of omitted elements of the raw matrix, and the preprocessing component is configured to generate the tensor data by modifying the raw data based on at least one of characteristics of the semiconductor manufacturing equipment and characteristics of the plurality of process parameters.

Semiconductor process modeling system and method

Measurement data are produced by measuring characteristics of a semiconductor device. Target parameters are selected among a plurality of parameters of a device model where the device model is configured to perform a simulation based on device data and output simulation result data indicating the characteristics of the semiconductor device. Initial value sets corresponding to different combinations of initial values of the target parameters are selected. Local minimum values are determined based on reinforcement learning. Each local minimum value corresponds to a minimum value of a difference between the measurement data and the simulation result data with respect to each initial value set. Optimal values of the target parameters are determined based on the plurality of local minimum values. The device model capable of precisely predicting characteristics of the semiconductor device is generated by determining the parameters of the device model using the optimization scheme based on the reinforcement learning.

Method of generating device model and computing device performing the same

A vehicle may include a spoiler mounted on or near a rear of a vehicle and an antenna apparatus provided inside the spoiler. The spoiler may include a spoiler body configured to include a seating portion on which the antenna apparatus is seated, and a cover configured to cover the seating portion. The antenna apparatus may include a casing, a cable electrically connected to an electric apparatus in the vehicle, a pole type antenna provided outside the casing, and an antenna assembly electrically connected to the cable and the pole type antenna and including at least one semiconductor element and a plurality of patch antennas mounted on a printed circuit board.

Vehicle and an antenna spoiler for a vehicle

Growing community of inventors

Seoul, South Korea

Seyoung Park