Systems and methods for multidimensional dynamic part average testing

Patent metadata

Field	Value
Publication number	US-12007428-B2
Application number	US-202117497518-A
Country	US
Kind code	B2
Filing date	Oct 8, 2021
Priority date	Oct 8, 2021
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

Abstract

Embodiments of the present invention provide systems and methods for multidimensional parts average testing for testing devices and analyzing testing results to detect outliers according to embodiments of the present invention. The testing can include calculating multivariate (e.g., bivariate) statistics using delta measurements of like devices, a ratio of measurements, or principal component analysis that identifies eigenvectors and eigenvalues to define meta parameters, for example. Raw test result data can be converted to residual space and robust regression can be performed to prevent outlier results from influencing regression, thereby reducing overkill advantageously.

First claim

What is claimed is:

1. A method of dynamic parts average testing, the method comprising:
determining testing limits based on historic testing result data;
testing a plurality of portions of a material according to the testing limits to obtain test results;
computing multivariate statistics using the test results;
computing mean or median values of the multivariate statistics;
computing differences between the multivariate statistics and the mean or median values;
updating the testing limits based on the mean or median values to produce updated testing limits;
identifying a trend indicating an out of control material drift of the material based on the mean or median values and the updated testing limits; and
halting testing of the plurality of portions of the material responsive to the identifying the trend.

2. The method as described in claim 1 , further comprising determining significant measurements for testing by performing at least one of: principal component analysis (PCA); independent component analysis (ICA); auto encoding; or machine learning.

3. The method as described in claim 1 , wherein the computing multivariate statistics using the test results comprises at least one of: forming pairs; forming ratios; and forming deltas.

4. The method as described in claim 1 , wherein the computing multivariate statistics using the test results comprises clustering the test results according to a result type.

5. The method as described in claim 1 , further comprising converting the mean or median values to residual space using a non-linear, non-monotonic transformation to amplify outlier results.

6. The method as described in claim 1 , further comprising removing outlier results before the updating the testing limits based on the mean or median values.

7. The method as described in claim 1 , further comprising performing reweighted least square regression before the updating the testing limits based on the mean or median values.

8. An apparatus for performing dynamic parts average testing, the apparatus comprising:
a processor; and
a memory in communication with the processor for storing test data and instructions, wherein the processor executes instructions to perform a method of multivariate parts average testing, the method comprising:
determining testing limits based on historic testing result data;
testing a plurality of portions of a material according to the testing limits to obtain test results;
computing multivariate statistics using the test results;
computing mean or median values of the multivariate statistics;
computing differences between the multivariate statistics and the mean or median values;
updating the testing limits based on the mean or median values to produce updated testing limits;
identifying a trend indicating an out of control material drift of the material based on the mean or median values and the updated testing limits; and
halting testing of the plurality of portions of the material responsive to the identifying the trend.

9. The apparatus as described in claim 8 , wherein the method further comprises determining significant measurements for testing by performing at least one of:
principal component analysis (PCA); auto encoding; or machine learning.

10. The apparatus as described in claim 8 , wherein the computing multivariate statistics using the test results comprises at least one of: forming bivariate pairs; forming bivariate ratios; and forming bivariate deltas.

11. The apparatus as described in claim 8 , wherein the computing multivariate statistics using the test results comprises clustering the test results according to a result type.

12. The apparatus as described in claim 8 , wherein the method further comprises converting the mean or median values to residual space using a non-linear, non-monotonic transformation to amplify outlier results.

13. The apparatus as described in claim 8 , wherein the method further comprises removing outlier results before the updating the testing limits based on the mean or median values.

14. The apparatus as described in claim 8 , wherein the method further comprises performing reweighted least square regression before the updating the testing limits based on the mean or median values.

15. One or more non-transitory computer-readable medium comprising program instructions stored thereon that are executable by one or more processors to implement a method comprising:
determining testing limits based on historic testing result data;
testing a plurality of portions of a material according to the testing limits to obtain test results;
computing multivariate statistics using the test results;
computing mean or median values of the multivariate statistics;
computing differences between the multivariate statistics and the mean or median values;
updating the testing limits based on the mean or median values to produce updated testing limits;
identifying a trend indicating an out of control material drift of the material based on the mean or median values and the updated testing limits; and
halting testing of the plurality of portions of the material responsive to the identifying the trend.

16. The non-transitory computer-readable medium as described in claim 15 , wherein the method further comprises determining significant measurements for testing by performing at least one of: principal component analysis (PCA); independent component analysis (ICA); auto encoding; or machine learning.

17. The non-transitory computer-readable medium as described in claim 15 , wherein the computing multivariate statistics using the test results comprises at least one of: forming bivariate pairs; forming ratios; and forming deltas.

18. The non-transitory computer-readable medium as described in claim 15 , wherein the computing multivariate statistics using the test results comprises clustering the test results according to a result type.

19. The non-transitory computer-readable medium as described in claim 15 , wherein the method further comprises converting the mean or median values to residual space using a non-linear, non-monotonic transformation to amplify outlier results.

20. The non-transitory computer-readable medium as described in claim 15 , wherein the determining testing limits based on historic testing result data comprises accessing at least one of: e-test data; data produced during a previous manufacturing step; statistically correlated data; or topologically correlated data.

Assignees

• Advantest Corp

Inventors

• Butler Kenneth
• Leventhal Ira
• Xanthopoulos Constantinos
• Hart Alan
• Buras Brian
• Schaub Keith

Classifications

• G06N20/00

  Machine learning · CPC title

• G01R31/2894 Primary

  Aspects of quality control [QC] (G01R31/31718 takes precedence; program control for QC G05B19/41875) · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G01R31/31718

  Logistic aspects, e.g. binning, selection, sorting of devices under test, tester/handler interaction networks, Test management software, e.g. software for test statistics or test evaluation, yield analysis (mechanical aspects G01R31/2808, G01R31/2851) · CPC title

• G01R31/01

  Subjecting similar articles in turn to test, e.g. "go/no-go" tests in mass production; Testing objects at points as they pass through a testing station (testing of cables continuously passing the testing apparatus G01R31/59; testing dielectric strength or breakdown voltage G01R31/12) · CPC title