公开(公告)号：US12135542B2

公开(公告)日：2024-11-05

申请号：US17979142

申请日：2022-11-02

Applicant: SanDisk Technologies LLC

Inventor： Tsuyoshi Sendoda ,  Yusuke Ikawa ,  Nagarjuna Asam ,  Kei Samura ,  Masaaki Higashitani

IPC: G05B19/418

Abstract: 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.

公开(公告)号：US12009269B2

公开(公告)日：2024-06-11

申请号：US17725695

申请日：2022-04-21

Applicant: SanDisk Technologies LLC

Inventor： Cheng-Chung Chu ,  Masaaki Higashitani ,  Yusuke Ikawa ,  Seyyed Ehsan Esfahani Rashidi ,  Kei Samura ,  Tsuyoshi Sendoda ,  Yanli Zhang

IPC: H01L21/66 ,  H01L27/11578 ,  H10B43/20 ,  H10B43/10

CPC classification number: H01L22/14 ,  H01L22/12 ,  H01L22/26 ,  H10B43/20 ,  H10B43/10

Abstract: 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.

公开(公告)号：US20220413036A1

公开(公告)日：2022-12-29

申请号：US17360573

申请日：2021-06-28

Applicant: SanDisk Technologies LLC

Inventor： Yusuke Ikawa ,  Tsuyoshi Sendoda ,  Kei Samura ,  Masaaki Higashitani

IPC: G01R31/27 ,  G01R31/3183 ,  G01R31/317 ,  G01R31/3181 ,  G06N3/063

Abstract: 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.

公开(公告)号：US12105137B2

公开(公告)日：2024-10-01

申请号：US17360573

申请日：2021-06-28

Applicant: SanDisk Technologies LLC

Inventor： Yusuke Ikawa ,  Tsuyoshi Sendoda ,  Kei Samura ,  Masaaki Higashitani

IPC: G01R31/27 ,  G01R31/317 ,  G01R31/3181 ,  G01R31/3183 ,  G06N3/063

CPC classification number: G01R31/275 ,  G01R31/31707 ,  G01R31/31813 ,  G01R31/31835 ,  G06N3/063

Abstract: 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.

公开(公告)号：US20230142936A1

公开(公告)日：2023-05-11

申请号：US18152669

申请日：2023-01-10

Applicant: SanDisk Technologies LLC

Inventor： Tsuyoshi Sendoda ,  Yusuke Ikawa ,  Nagarjuna Asam ,  Kei Samura ,  Masaaki Higashitani

IPC: G06T7/00 ,  G06N20/20

CPC classification number: G06T7/0002 ,  G06N20/20

Abstract: 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.

公开(公告)号：US20230054342A1

公开(公告)日：2023-02-23

申请号：US17979142

申请日：2022-11-02

Applicant: SanDisk Technologies LLC

Inventor： Tsuyoshi Sendoda ,  Yusuke Ikawa ,  Nagarjuna Asam ,  Kei Samura ,  Masaaki Higashitani

IPC: G05B19/418

Abstract: 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.

公开(公告)号：US20220415718A1

公开(公告)日：2022-12-29

申请号：US17725695

申请日：2022-04-21

Applicant: SanDisk Technologies LLC

Inventor： Cheng-Chung Chu ,  Masaaki Higashitani ,  Yusuke Ikawa ,  Seyyed Ehsan Esfahani Rashidi ,  Kei Samura ,  Tsuyoshi Sendoda ,  Yanli Zhang

IPC: H01L21/66 ,  H01L27/11578

Abstract: 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.