METHOD FOR CONVEYING HIGH FREQUENCY MODULE AND A HIGH-FREQUENCY MODULE

    公开(公告)号:US20200273821A1

    公开(公告)日:2020-08-27

    申请号:US16282304

    申请日:2019-02-22

    摘要: A method and a high-frequency module that includes a high frequency die that may include multiple die pads; a substrate that may include a first buildup layer, a second buildup layer and a core that is positioned between the first buildup layer and a second buildup layer; a line card that may include multiple line card pads; and multiple conductors that pass through the substrate without reaching a majority of a depth of the core, and couple the multiple die pads to the multiple line card pads.

    Method for conveying high frequency module and a high-frequency module

    公开(公告)号:US11101226B2

    公开(公告)日:2021-08-24

    申请号:US16282304

    申请日:2019-02-22

    摘要: A method and a high-frequency module that includes a high frequency die that may include multiple die pads; a substrate that may include a first buildup layer, a second buildup layer and a core that is positioned between the first buildup layer and a second buildup layer; a line card that may include multiple line card pads; and multiple conductors that pass through the substrate without reaching a majority of a depth of the core, and couple the multiple die pads to the multiple line card pads.

    High-frequency module
    3.
    发明授权

    公开(公告)号:US10985118B2

    公开(公告)日:2021-04-20

    申请号:US16282306

    申请日:2019-02-22

    摘要: A method and a high-frequency module that includes (a) a high frequency die that includes multiple die pads, (b) a substrate that comprises a first buildup layer, a second buildup layer and a core that is positioned between the first buildup layer and a second buildup layer, (c) a heat sink and coupling module that comprises a heat sink and multiple first conductors that pass through the heat sink and extend outside the heat sink; (d) a line card that comprises multiple line card pads that are coupled to external ends of the multiple first conductors; (e) coupling elements that are coupled to internal end of the multiple first conductors; and (f) multiple second conductors that pass through the substrate without reaching a majority of a depth of the core, and couple the multiple die pads to the coupling elements. The high frequency it not lower than fifty gigabits per second.

    HIGH-FREQUENCY MODULE
    4.
    发明申请

    公开(公告)号:US20200273822A1

    公开(公告)日:2020-08-27

    申请号:US16282306

    申请日:2019-02-22

    摘要: A method and a high-frequency module that includes (a) a high frequency die that includes multiple die pads, (b) a substrate that comprises a first buildup layer, a second buildup layer and a core that is positioned between the first buildup layer and a second buildup layer, (c) a heat sink and coupling module that comprises a heat sink and multiple first conductors that pass through the heat sink and extend outside the heat sink; (d) a line card that comprises multiple line card pads that are coupled to external ends of the multiple first conductors; (e) coupling elements that are coupled to internal end of the multiple first conductors; and (f) multiple second conductors that pass through the substrate without reaching a majority of a depth of the core, and couple the multiple die pads to the coupling elements. The high frequency it not lower than fifty gigabits per second.

    CAUSAL EXPLANATION OF ATTENTION-BASED NEURAL NETWORK OUTPUT

    公开(公告)号:US20230325628A1

    公开(公告)日:2023-10-12

    申请号:US18325267

    申请日:2023-05-30

    IPC分类号: G06N3/02

    CPC分类号: G06N3/02

    摘要: Causal explanations of outputs of a neural network can be learned from an attention layer in the neural network. The neural network may compute an output variable by processing a variable set including one or more input variables. An attention matrix may be computed by the attention layer in an abductive inference for which a new variable set including the input variables and the output variable is input into the neural network. Causal relationship between the variables in the new variable set may be determined based on the attention matrix and illustrated in a causal graph. A tree structure may be generated based on the causal graph. An input variable may be identified using the tree structure and determined to be the reason why the neural network computed the output variable. An explanation of the causal relation between the input variable and output variable can be generated and provided.