USRE42481EExpiredUtility
Semiconductor yield management system and method
Est. expiryDec 8, 2019(expired)· nominal 20-yr term from priority
H10P 74/23
47
PatentIndex Score
2
Cited by
23
References
34
Claims
Abstract
A system and method for yield management is disclosed wherein a data set containing one or more prediction variable values and one or more response values is input into the system. The system can pre-process the input data set to remove prediction variables with missing values and data sets with missing values. The pre-processed data can then be used to generate a model that may be a decision tree. The system can accept user input to modify the generated model. Once the model is complete, one or more statistical analysis tools can be used to analyze the data and generate a list of the key yield factors for the particular data set.
Claims
exact text as granted — not AI-modified1. A computerized yield management system, comprising:
pre-processing computing device means executed by a data pre-processor in a computer processing unit for pre-processing an input data set comprising one or more prediction variables and one or more response variables containing data about a particular semiconductor process, the pre-processing computing device means further comprising means for removing one or more prediction variables from the input data set having more than a predetermined number of missing values, means for removing one or more prediction variables from the input data set having more than a predetermined number of classes, and means for removing data having more than a predetermined number of missing values to generate pre-processed data;
model generating computer device means executed by a model builder in the computer processing unit, the model builder being in communication with the data pre-processor, for generating a model based on the pre-processed data, the model being a decision tree;
computing device means executed by the model builder for modifying the model based on user input; and
computing device means executed by a statistical tool library in the computer processing unit, the statistical tool library being in communication with the model builder, for analyzing the model using a statistical tool to generate one or more key yield factors based on the input data set.
2. The system of claim 1 , wherein the model generating computing device means further comprises means for building a decision tree containing a root node, one or more intermediate nodes and one or more terminal nodes wherein a response value at the one or more terminal nodes is presented to the user and splitting means for splitting a node in the tree into one or more sub-nodes based on prediction variables contained in the node.
3. The system of claim 2 , wherein the splitting means further comprises means for determining if a number of data cases in a node are less than a predetermined threshold value, means for calculating a goodness of split value for splitting the node based on each predictor prediction variable in the node, means for selecting prediction variables having a maximum goodness of split value and means for splitting the node into one or more sub-nodes based on the prediction variables having the maximum goodness of split value.
4. The system of claim 3 , wherein the one or more prediction variables and the one or more response variables are categorical variables.
5. The system of claim 4 , wherein the splitting means further comprises a splitting rule and a goodness of split rule, the splitting rule comprising means for placing a case into a left sub-node if the case is included in the values of the predictor prediction variable and wherein the goodness of split rule is of the form:
Φ
(
s
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g
(
T
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N
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g
(
T
L
)
-
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N
T
g
(
T
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where Φ(s) represents a goodness of split rule for a split, s; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in a left sub-node of node T; and N T R is a number of cases in a right sub-node of node T.
6. The system of claim 3 , wherein the one or more prediction variables and the one or more response variables are numerical.
7. The system of claim 6 , wherein the splitting means further comprises a splitting rule and a goodness of split rule, the splitting rule comprising means for placing a case into a left sub-node if the value of the predictor prediction variable for a particular case is less than or equal to a first predetermined value of the predictor prediction variables or if the value of the predictor prediction variable for the particular case is between the first predetermined value and a second predetermined value of the predictor prediction variables and wherein the goodness of split rule is of the form:
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=
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if
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c
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if
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where Φ(S*) represents a goodness of split rule for a split, S*; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in a left sub-node of node T; N T R is a number of cases in a right sub-node of node T; c is a user-selectable variable between 0 and 1.
8. The system of claim 3 , wherein the a response variable from the one or more response variables is a categorical variable and the a prediction variable from the one or more prediction variables is a numerical variable.
9. The system of claim 8 , wherein the splitting means further comprises a splitting rule and a goodness of split rule, the splitting rule comprising means for placing a case into a left sub-node if the value of the predictor prediction variable for a particular case is less than or equal to a first predetermined value of the predictor prediction variables or if the value of the predictor prediction variable for the particular case is between the first predetermined value and a second predetermined value of the predictor prediction variables and wherein the goodness of split rule is of the form:
Φ
(
S
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=
{
g
(
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g
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)
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(
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if
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first
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c
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g
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-
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g
(
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if
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has
a
second
form
where Φ(S) represents a goodness of split rule for a split, S; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in the left sub-node of node T; N T R is a number of cases in the right sub-node of node T; c is a user-selectable variable between 0 and 1.
10. The system of claim 3 , wherein the a response variable from the one or more response variables is a numerical variable and the a prediction variable from the one or more prediction variables is a categorical variable.
11. The system of claim 10 , wherein the splitting means further comprises a splitting rule and a goodness of split rule, the splitting rule comprising means for placing a case into a left sub-node if the case is included in the values of the predictor prediction variable and wherein the goodness of split rule is of the form:
Φ
(
s
)
=
g
(
T
)
-
N
T
L
N
T
g
(
T
L
)
-
N
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R
N
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g
(
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where Φ(s) represents a goodness of split rule for a split, s; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in the left sub-node of node T; and N T R is a number of cases in the right sub-node of node T.
12. A yield management method, comprising:
pre-processing, via a computing device, an input data set comprising one or more prediction variables and one or more response variables containing data about a particular semiconductor process, the pre-processing further comprising removing one or more prediction variables from the input data set having more than a predetermined number of missing values, removing one or more prediction variables from the input data set having more than a predetermined number of classes and removing data having more than a predetermined number of missing values to generate pre-processed data;
generating, via the computing device, a model based on the pre-processed data, the model being a decision tree;
modifying, via the computing device, the model based on user input; and
analyzing, via the computing device, the model using statistical tools to examine one or more key yield factors based on the input data set.
13. The method of claim 12 , wherein the generated model further comprises building a decision tree containing a root node, one or more intermediate nodes and one or more terminal nodes wherein a response value at the one or more terminal nodes is presented to the user and splitting a node in the tree into one or more sub-nodes based on prediction variables contained in the node.
14. The method of claim 13 , wherein the splitting further comprises determining if a number of data cases in a node are less than a predetermined threshold value, calculating a goodness of split value for splitting the node based on each predictor prediction variable in the node, selecting prediction variables having a maximum goodness of split value and splitting the node into one or more sub-nodes based on the prediction variables having the maximum goodness of split value.
15. The method of claim 14 , wherein the one or more prediction variables and the one or more response variables are categorical variables.
16. The method of claim 15 , wherein the splitting further comprises a splitting rule and a goodness of split rule, the splitting rule comprising placing a case into a left sub-node if the case is included in the values of the predictor prediction variable and wherein the goodness of split rule is of the form:
Φ
(
s
)
=
g
(
T
)
-
N
T
L
N
T
g
(
T
L
)
-
N
T
R
N
T
g
(
T
R
)
where Φ(s) represents a goodness of split rule for a split, s; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in the left sub-node of node T; and N T R is a number of cases in the right sub-node of node T.
17. The method of claim 14 , wherein the one or more prediction variables and the one or more response variables are numerical.
18. The method of claim 17 , wherein the splitting further comprises a splitting rule and a goodness of split rule, the splitting rule comprising placing a case into a left sub-node if the value of the predictor prediction variable for a particular case is less than or equal to a first predetermined value of the predictor prediction variables or if the value of the predictor prediction variable for the particular case is between the first predetermined value and a second predetermined value of the predictor prediction variables and wherein the goodness of split rule is of the form:
Φ
(
S
*
)
=
{
g
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T
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)
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if
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first
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c
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if
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has
a
second
form
where Φ(S*) represents a goodness of split rule for a split, S*; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in a left sub-node of node T; N T R is a number of cases in a right sub-node of node T; c is a user-selectable variable between 0 and 1.
19. The method of claim 14 , wherein the a response variable from the one or more response variables is a categorical variable and the a prediction variable from the one or more prediction variables is a numerical variable.
20. The method of claim 19 , wherein the splitting further comprises a splitting rule and a goodness of split rule the splitting rule comprising placing a case into a left sub-node if the value of the predictor prediction variable for a particular case is less than or equal to a first predetermined value of the predictor prediction variables or if the value of the predictor prediction variable for the particular case is between the first predetermined value and a second predetermined value of the predictor prediction variables and wherein the goodness of split rule is of the form:
Φ
(
S
)
=
{
g
(
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)
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(
T
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)
-
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R
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(
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if
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has
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first
form
c
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T
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)
-
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T
R
N
T
g
(
T
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)
)
if
S
has
a
second
form
where Φ(S) represents a goodness of split rule for a split, S; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in the left sub-node of node T; N T R is a number of cases in the right sub-node of node T; c is a user-selectable variable between 0 and 1.
21. The method of claim 14 , wherein the a response variable from the one or more response variables is a numerical variable and the a prediction variable from the one or more prediction variables is a categorical variable.
22. The method of claim 21 , wherein the splitting further comprises a splitting rule and a goodness of split rule, the splitting rule comprising placing a case into a left sub-node if the case is included in the values of the predictor prediction variable and wherein the goodness of split rule is of the form:
Φ
(
s
)
=
g
(
T
)
-
N
T
L
N
T
g
(
T
L
)
-
N
T
R
N
T
g
(
T
R
)
where Φ(s) represents a goodness of split rule for a split, s; g(T) represents noisiness of a node T; g(T L ) represents noisiness of a left sub-node of node T; g(T R ) represents noisiness of a right sub-node of node T; N T is a number of cases in node T; N T L is a number of cases in the left sub-node of node T; and N T R is a number of cases in the right sub-node of node T.
23. A computerized yield management system, comprising:
pre-processing computing device means executed by a data pre-processor in a computer processing unit for pre-processing an input data set comprising one or more prediction variables and one or more response variables containing data about a particular semiconductor process to remove data having at least a predetermined number of missing values to generate pre-processed data, and the pre-processing computing device means comprising means for removing one or more prediction variables from the input data set having more than a predetermined number of classes; computing device means executed by a model builder in the computer processing unit, the model builder being in communication with the data pre-processor, for generating a model based on the pre-processed data, the model being a decision tree identifying one or more variables as key yield factors; and computing device means executed by a statistical tool library in the computer processing unit, the statistical tool library being in communication with the model builder, for analyzing the model using a statistical tool to examine one or more key yield factors based on the input data set.
24. The system of claim 23 wherein the pre-processing computing device means further comprises means for removing one or more prediction variables from the input data set having more than a predetermined number of missing values.
25. The system of claim 23 wherein the pre-processing computing device means further comprises means for removing data having more than a predetermined number of missing values.
26. The system of claim 23, further comprising means for modifying the model based on user input.
27. A yield management method, comprising:
pre-processing, via a computing device, an input data set comprising one or more prediction variables and one or more response variables containing data about a particular semiconductor process to remove data having at least a predetermined number of missing values to generate pre-processed data, the pre-processing further comprising removing one or more prediction variables from the input data set having more than a predetermined number of classes; generating, via the computing device, a model based on the pre-processed data, the model being a decision tree identifying one or more variables as key yield factors; and analyzing the model using a statistical tool to examine one or more key yield factors based on the input data set.
28. The method of claim 27 wherein the pre-processing further comprises removing one or more prediction variables from the input data set having more than a predetermined number of missing values.
29. The method of claim 27 wherein the pre-processing further comprises removing data having more than a predetermined number of missing values.
30. The method of claim 27, further comprising modifying the model based on user input.
31. A computerized yield management system, comprising:
pre-processing computing device means executed by a data pre-processor in a computer processing unit for pre-processing an input data set comprising one or more prediction variables and one or more response variables containing data about a particular semiconductor process to remove data having at least a predetermined number of missing values to generate pre-processed data, and the pre-processing computing device means comprising means for removing one or more prediction variables from the input data set having more than a predetermined number of classes; computing device means executed by a model builder in the computer processing unit, the model builder being in communication with the data pre-processor, for generating a model based on the pre-processed data, the model being a decision tree identifying one or more variables as key yield factors; and computing device means executed by a statistical tool in the computer processing unit, the statistical tool being in communication with the model builder, for analyzing the model using the statistical tool to generate yield management information.
32. The system of claim 31 wherein the pre-processing computing device means further comprises means for removing data containing erroneous values.
33. The system of claim 31 wherein the pre-processing computing device means further comprises means for removing data containing invalid values.
34. The system of claim 31, further comprising means for modifying the model based on user input.Cited by (0)
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