TELEMETRY-BASED MODEL DRIVEN MANUFACTURING TEST METHODOLOGY

    公开(公告)号:US20230076130A1

    公开(公告)日:2023-03-09

    申请号:US17590518

    申请日:2022-02-01

    Abstract: An approach is presented herein to use an in-situ algorithmic decision methodology during each stage of testing before 2C/4C to decide how long to test, how much margin should be used for each device under the test (DUT) to shorten or eliminate 2C/4C testing. Each DUT will be tested differently based on the risk level or the likelihood of failure at 2C/4C. To be able to achieve this, low-level hardware (HW) based sensors (on the printed circuit board assembly (PCBA), in power module, in silicon components, in silicon component complex, etc.) are used to collect telemetry data with a high frequency data acquisition rate during the testing. As testing is ongoing for each DUT, a margin distribution and algorithm modeling is performed in-situ.

    Telemetry-based model driven manufacturing test methodology

    公开(公告)号:US12164400B2

    公开(公告)日:2024-12-10

    申请号:US17590518

    申请日:2022-02-01

    Abstract: An approach is presented herein to use an in-situ algorithmic decision methodology during each stage of testing before 2C/4C to decide how long to test, how much margin should be used for each device under the test (DUT) to shorten or eliminate 2C/4C testing. Each DUT will be tested differently based on the risk level or the likelihood of failure at 2C/4C. To be able to achieve this, low-level hardware (HW) based sensors (on the printed circuit board assembly (PCBA), in power module, in silicon components, in silicon component complex, etc.) are used to collect telemetry data with a high frequency data acquisition rate during the testing. As testing is ongoing for each DUT, a margin distribution and algorithm modeling is performed in-situ.

    TELEMETRY DATA BASED COUNTERFEIT DEVICE DETECTION

    公开(公告)号:US20240320691A1

    公开(公告)日:2024-09-26

    申请号:US18187520

    申请日:2023-03-21

    CPC classification number: G06Q30/0185 H04Q9/00

    Abstract: Techniques are described for detecting counterfeit products by identifying differences between hardware components and orientations of the counterfeit products, and hardware components and orientations of authentic products. In some examples, the hardware components and orientations can be identified by generating hardware intrinsic development data based on telemetry data of products (or “devices”). By way of example, the telemetry data may be analyzed by machine learning (ML) models to generate representative models of the hardware intrinsic development data. In various examples, the representative models can include sample representative models of hardware intrinsic development data generated based on valid telemetry data of authentic devices. In those or other examples, the representative models can include other representative models (or “test representative models”) of hardware intrinsic development data generated based on unvalidated telemetry data of test devices. Comparisons between the representative models can be utilized to identify the counterfeit products.

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