SEMICONDUCTOR DEVICE
    1.
    发明公开

    公开(公告)号:US20230275081A1

    公开(公告)日:2023-08-31

    申请号:US18313014

    申请日:2023-05-05

    CPC classification number: H01L27/0207 G06F30/392 H01L27/0924 H01L23/5286

    Abstract: A semiconductor device includes first and second cell rows, first to fourth fin shaped structures. The first cell row has a first row height. The second cell row is adjacent with the first cell row, and having a second row height. The first fin shaped structure extends across the first cell row. The second fin shaped structure extends across the first cell row, and separated from the first fin shaped structure. The third fin shaped structure extends across the second cell row. The fourth fin shaped structure extends across the second cell row, and separated from the third fin shaped structure. The first to fourth fin shaped structures are arranged in order along the first direction, each of the first, second and fourth fin shaped structure has a first conductive type, the third fin shaped structure has a second conductive type.

    MACHINE-LEARNING DESIGN ENABLEMENT PLATFORM
    4.
    发明申请

    公开(公告)号:US20180268096A1

    公开(公告)日:2018-09-20

    申请号:US15724663

    申请日:2017-10-04

    Abstract: Electronic design automation (EDA) of the present disclosure, in various embodiments, optimizes designing, simulating, analyzing, and verifying of one or more electronic architectural designs for an electronic device. The EDA of the present disclosure identifies one or more electronic architectural features from the one or more electronic architectural designs. In some situations, the EDA of the present disclosure can manipulate one or more electronic architectural models over multiple iterations using a machine learning process until one or more electronic architectural models from among the one or more electronic architectural models satisfy one or more electronic design targets. The EDA of the present disclosure substitutes the one or more electronic architectural models that satisfy the one or more electronic design targets for the one or more electronic architectural features in the one or more electronic architectural designs to optimize the one or more electronic architectural designs. The EDA of the present disclosure can substitute the one or more electronic architectural models before, during, and/or after designing, simulating, analyzing, and/or verifying of the one or more electronic architectural designs to effectively decrease the time to market (TTM) for the electronic device.

    Machine-Learning Design Enablement Platform
    7.
    发明申请

    公开(公告)号:US20200272777A1

    公开(公告)日:2020-08-27

    申请号:US16871841

    申请日:2020-05-11

    Abstract: Electronic design automation (EDA) of the present disclosure, in various embodiments, optimizes designing, simulating, analyzing, and verifying of one or more electronic architectural designs for an electronic device. The EDA of the present disclosure identifies one or more electronic architectural features from the one or more electronic architectural designs. In some situations, the EDA of the present disclosure can manipulate one or more electronic architectural models over multiple iterations using a machine learning process until one or more electronic architectural models from among the one or more electronic architectural models satisfy one or more electronic design targets. The EDA of the present disclosure substitutes the one or more electronic architectural models that satisfy the one or more electronic design targets for the one or more electronic architectural features in the one or more electronic architectural designs to optimize the one or more electronic architectural designs. The EDA of the present disclosure can substitute the one or more electronic architectural models before, during, and/or after designing, simulating, analyzing, and/or verifying of the one or more electronic architectural designs to effectively decrease the time to market (TTM) for the electronic device.

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