SEMICONDUCTOR DEVICE
    5.
    发明公开

    公开(公告)号:US20230422479A1

    公开(公告)日:2023-12-28

    申请号:US18133964

    申请日:2023-04-12

    CPC classification number: H10B12/315 H10B12/34 H10B12/482

    Abstract: A semiconductor device includes a first active pattern included in an upper portion of a substrate in a memory cell region, and having an isolated shape extending so that a direction oblique to a first direction is a major axis direction of the first active pattern. A first device isolation pattern provided inside a first trench included in the substrate, and covering a side wall of the first active pattern is provided. A first gate structure is provided inside a gate trench extending in the first direction on upper portions of the first active pattern and the first device isolation pattern. A barrier impurity region is selectively formed only on surfaces of both side walls of a major axis of the first active pattern. First and second impurity regions are provided on the upper portion of the first active pattern adjacent to both sides of the first gate structure.

    METHOD FOR PREDICTING DEFECT IN SEMICONDUCTOR DEVICE

    公开(公告)号:US20220268830A1

    公开(公告)日:2022-08-25

    申请号:US17495487

    申请日:2021-10-06

    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.

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