FAILURE DIAGNOSIS SUPPORT TECHNIQUE

• US 20120239347A1
• Filed: 03/09/2012
• Published: 09/20/2012
• Est. Priority Date: 03/18/2011
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: NITTA, Izumi
• Application Number:
  US13/416,370
• Publication Number:
  US 20120239347A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G11C 29/56008 Error analysis, representation of errors

Abstract

The disclosed method includes: calculating a first expected value of the number of failures for each combination of a feature that is a failure factor and a first group regarding classification elements of first semiconductor devices for which a failure is analyzed and second semiconductors on which a same circuit as the first semiconductors is implemented, from first data for each first group and a predetermined expression, wherein the first data includes the number of actual failures occurred in the first group and first feature values of features; and calculating, for each feature, a first indicator value representing similarity between a distribution of the first expected values over the first groups and a distribution of the numbers of actual failures over the first groups, from the first expected value for each combination of the feature and the first group and the number of actual failures for each first group.

Claims

1. A computer-readable, non-transitory storage medium storing a program for causing a computer to execute a procedure comprising:

calculating a first expected value of the number of failures for each combination of a feature of a plurality of features that are failure factors and a first group of a plurality of first groups regarding classification elements of first semiconductor devices for which a failure is analyzed and second semiconductors on which a same circuit as the first semiconductors is implemented, from first data for each of the plurality of first groups and a predetermined expression, wherein the first data includes the number of actual failures occurred in the first group and first feature values of the plurality of features; and

calculating, for each of the plurality of features, a first indicator value representing similarity between a distribution of the first expected values over the plurality of first groups and a distribution of the numbers of actual failures over the plurality of first groups, from the first expected value for each combination of the feature and the first group and the number of actual failures for each of the plurality of first groups.

2. The computer-readable, non-transitory storage medium as set forth in claim 1, wherein the procedure further comprises:

calculating a second expected value of the number of failures for each combination of the feature and a second group of a plurality of second groups regarding classification elements of the first semiconductor devices and third semiconductors manufactured by using a same process as the first semiconductors, from second data for each of the plurality of second groups and the predetermined expression, wherein the second data includes the number of actual failures occurred in the second group and second feature values of the plurality of features; and

calculating, for each of the plurality of features, a second indicator value representing similarity between a distribution of the second expected values over the plurality of second groups and a distribution of the numbers of actual failures over the plurality of second groups, from the second expected value for each combination of the feature and the second group and the number of actual failures for each of the plurality of second groups.

3. The computer-readable, non-transitory storage medium as set forth in claim 2, wherein the procedure further comprises:

identifying first features for which the first indicator values satisfying a predetermined first condition are calculated;

calculating a first regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for third data for each third group of a plurality of third groups regarding classification elements of the first semiconductors, after setting 1 to weights of the identified first features and setting a value less than 1 to weights of features other than the identified first features, wherein the third data includes the number of actual failures occurred in the third group and third feature values of the plurality of features;

identifying second features for which the second indicator values satisfying a predetermined second condition are calculated;

calculating a second regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the identified second features and setting 0 to weights of features other than the identified second features;

calculating a third regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the plurality of features; and

identifying an regression expression whose goodness-of-fit index for the third data is the greatest from among the first regression expression, the second regression expression and the third regression expression.

4. The computer-readable, non-transitory storage medium as set forth in claim 3, wherein the procedure further comprises:

calculating a prediction value of the failure occurrence probability for each of the plurality of third groups according to the identified regression expression;

identifying a top N third groups in descending order of the prediction value, wherein the N is an integer;

calculating, for each of the plurality of features, a total sum of values of a term of the feature in the identified regression expression by using data of the top N third groups; and

sorting the plurality of features in descending order of the calculated total sum.

5. A failure diagnosis support method comprising:

calculating, by using a computer, a first expected value of the number of failures for each combination of a feature of a plurality of features that are failure factors and a first group of a plurality of first groups regarding classification elements of first semiconductor devices for which a failure is analyzed and second semiconductors on which a same circuit as the first semiconductors is implemented, from first data for each of the plurality of first groups and a predetermined expression, wherein the first data includes the number of actual failures occurred in the first group and first feature values of the plurality of features; and

calculating, by using the computer, for each of the plurality of features, a first indicator value representing similarity between a distribution of the first expected values over the plurality of first groups and a distribution of the numbers of actual failures over the plurality of first groups, from the first expected value for each combination of the feature and the first group and the number of actual failures for each of the plurality of first groups.

6. The failure diagnosis support method as set forth in claim 5, further comprising:

calculating, by using the computer, a second expected value of the number of failures for each combination of the feature and a second group of a plurality of second groups regarding classification elements of the first semiconductor devices and third semiconductors manufactured by using a same process as the first semiconductors, from second data for each of the plurality of second groups and the predetermined expression, wherein the second data includes the number of actual failures occurred in the second group and second feature values of the plurality of features; and

calculating, by using the computer, for each of the plurality of features, a second indicator value representing similarity between a distribution of the second expected values over the plurality of second groups and a distribution of the numbers of actual failures over the plurality of second groups, from the second expected value for each combination of the feature and the second group and the number of actual failures for each of the plurality of second groups.

7. The failure diagnosis support method as set forth in claim 6, further comprising:

identifying, by using the computer, first features for which the first indicator values satisfying a predetermined first condition are calculated;

calculating, by using the computer, a first regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for third data for each third group of a plurality of third groups regarding classification elements of the first semiconductors, after setting 1 to weights of the identified first features and setting a value less than 1 to weights of features other than the identified first features, wherein the third data includes the number of actual failures occurred in the third group and third feature values of the plurality of features;

identifying, by using the computer, second features for which the second indicator values satisfying a predetermined second condition are calculated;

calculating, by using the computer, a second regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the identified second features and setting 0 to weights of features other than the identified second features;

calculating, by using the computer, a third regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the plurality of features; and

identifying, by using the computer, an regression expression whose goodness-of-fit index for the third data is the greatest from among the first regression expression, the second regression expression and the third regression expression.

8. The failure diagnosis support method as set forth in claim 7, further comprising:

calculating, by using the computer, a prediction value of the failure occurrence probability for each of the plurality of third groups according to the identified regression expression;

identifying, by using the computer, a top N third groups in descending order of the prediction value, wherein the N is an integer;

calculating, by using the computer, for each of the plurality of features, a total sum of values of a term of the feature in the identified regression expression by using data of the top N third groups; and

sorting, by using the computer, the plurality of features in descending order of the calculated total sum.

9. A failure diagnosis support apparatus comprising:

a memory;

a processing unit using the memory and configured to execute a procedure;

calculating a first expected value of the number of failures for each combination of a feature of a plurality of features that are failure factors and a first group of a plurality of first groups regarding classification elements of first semiconductor devices for which a failure is analyzed and second semiconductors on which a same circuit as the first semiconductors is implemented, from first data for each of the plurality of first groups and a predetermined expression, wherein the first data includes the number of actual failures occurred in the first group and first feature values of the plurality of features; and

calculating, for each of the plurality of features, a first indicator value representing similarity between a distribution of the first expected values over the plurality of first groups and a distribution of the numbers of actual failures over the plurality of first groups, from the first expected value for each combination of the feature and the first group and the number of actual failures for each of the plurality of first groups.

10. The failure diagnosis support apparatus as set forth in claim 9, wherein the procedure further comprises:

calculating a second expected value of the number of failures for each combination of the feature and a second group of a plurality of second groups regarding classification elements of the first semiconductor devices and third semiconductors manufactured by using a same process as the first semiconductors, from second data for each of the plurality of second groups and the predetermined expression, wherein the second data includes the number of actual failures occurred in the second group and second feature values of the plurality of features; and

calculating, for each of the plurality of features, a second indicator value representing similarity between a distribution of the second expected values over the plurality of second groups and a distribution of the numbers of actual failures over the plurality of second groups, from the second expected value for each combination of the feature and the second group and the number of actual failures for each of the plurality of second groups.

11. The failure diagnosis support apparatus as set forth in claim 10, wherein the procedure further comprises:

identifying first features for which the first indicator values satisfying a predetermined first condition are calculated;

calculating a first regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for third data for each third group of a plurality of third groups regarding classification elements of the first semiconductors, after setting 1 to weights of the identified first features and setting a value less than 1 to weights of features other than the identified first features, wherein the third data includes the number of actual failures occurred in the third group and third feature values of the plurality of features;

identifying second features for which the second indicator values satisfying a predetermined second condition are calculated;

calculating a second regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the identified second features and setting 0 to weights of features other than the identified second features;

calculating a third regression expression for calculating a failure occurrence probability using the plurality of features as variables, by carrying out a regression analysis for the third data, after setting 1 to weights of the plurality of features; and

identifying an regression expression whose goodness-of-fit index for the third data is the greatest from among the first regression expression, the second regression expression and the third regression expression.

12. The failure diagnosis support apparatus as set forth in claim 11, wherein the procedure further comprises:

calculating a prediction value of the failure occurrence probability for each of the plurality of third groups according to the identified regression expression;

identifying a top N third groups in descending order of the prediction value, wherein the N is an integer;

calculating, for each of the plurality of features, a total sum of values of a term of the feature in the identified regression expression by using data of the top N third groups; and

sorting the plurality of features in descending order of the calculated total sum.