CIRCUITRY ARRANGEMENT IN A FLOORPLAN OF A MEMORY DEVICE

    公开(公告)号:US20230367944A1

    公开(公告)日:2023-11-16

    申请号:US17740705

    申请日:2022-05-10

    摘要: The memory device includes a die with a first set of planes and a second set of planes. The planes are rectangular in shape with a major dimension and a minor dimension. The die includes a CMOS layer with at least one common peripheral circuitry area, and each of the planes includes a non-common peripheral circuitry area in the CMOS layer. Each plane of the first set of planes is oriented such that its major dimension extends in a first direction, and each plane of the second set of planes is oriented such that its major dimension extends in a second direction that is different than the first direction such that the non-common peripheral circuitry area of each plane is immediately adjacent the at least one common peripheral circuitry area in the CMOS layer.

    MODELLING AND PREDICTION SYSTEM WITH AUTO MACHINE LEARNING  IN THE PRODUCTION OF MEMORY DEVICES

    公开(公告)号:US20230142936A1

    公开(公告)日:2023-05-11

    申请号:US18152669

    申请日:2023-01-10

    IPC分类号: G06T7/00 G06N20/20

    CPC分类号: G06T7/0002 G06N20/20

    摘要: To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

    MODELLING AND PREDICTION OF VIRTUAL INLINE QUALITY CONTROL IN THE PRODUCTION OF MEMORY DEVICES

    公开(公告)号:US20230054342A1

    公开(公告)日:2023-02-23

    申请号:US17979142

    申请日:2022-11-02

    IPC分类号: G05B19/418

    摘要: To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

    Three-dimensional memory device with double-sided stepped surfaces and method of making thereof

    公开(公告)号:US11569259B2

    公开(公告)日:2023-01-31

    申请号:US16985305

    申请日:2020-08-05

    摘要: A memory die can include an alternating stack of insulating layers and electrically conductive layers located over a substrate, and memory stack structures vertically extending through the alternating stack. A first layer stack within the alternating stack includes a first staircase region in which the first electrically conductive layers have respective lateral extents that increase with a vertical distance from the substrate to provide first stepped surfaces. A second layer stack within the alternating stack includes a second staircase region in which the second electrically conductive layers have respective lateral extents that decrease with the vertical distance from the substrate to provide second stepped surfaces. The second layer stack can be more distal from the substrate than the first layer stack. Contact via structures can be formed from the top side and the bottom side of the alternating stack.

    VIRTUAL METROLOGY FOR FEATURE PROFILE PREDICTION IN THE PRODUCTION OF MEMORY DEVICES

    公开(公告)号:US20220415718A1

    公开(公告)日:2022-12-29

    申请号:US17725695

    申请日:2022-04-21

    IPC分类号: H01L21/66 H01L27/11578

    摘要: To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.