Semiconductor yield management system and method
DCFirst Claim
Patent Images
1. A yield management system, comprising:
- means 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 means further comprising means for removing prediction variables having more than a predetermined number of missing values, means for removing prediction variables 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;
means for generating a model based on the pre-processed data, the model being a decision tree;
means for modifying the model based on user input; and
means for analyzing the model using a statistical tool to generate one or more key yield factors based on the input data set.
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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.
35 Citations
22 Claims
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1. A yield management system, comprising:
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means 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 means further comprising means for removing prediction variables having more than a predetermined number of missing values, means for removing prediction variables 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;
means for generating a model based on the pre-processed data, the model being a decision tree;
means for modifying the model based on user input; and
means for analyzing the model using a statistical tool to generate one or more key yield factors based on the input data set. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
where Φ
(s) represents a goodness of split rule for a split, s;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in a left sub-node of node T; and
NTR is a number of cases in a right sub-node of node T.
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6. The system of claim 3, wherein the prediction and response variables are numerical.
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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 variable for a particular case is less than or equal to a first predetermined value of the predictor variables or if the value of the predictor variable for the particular case is between the first predetermined value and a second predetermined value of the predictor variables and wherein the goodness of split rule is of the form:
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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(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in a left sub-node of node T;
NTR is a number of cases in a right sub-node of node T;
c is a user-selectable variable between 0 and 1.
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8. The system of claim 3, wherein the response variable is a categorical variable and the prediction variable is a numerical variable.
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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 variable for a particular case is less than or equal to a first predetermined value of the predictor variables or if the value of the predictor variable for the particular case is between the first predetermined value and a second predetermined value of the predictor variables and wherein the goodness of split rule is of the form:
-
where Φ
(S) represents a goodness of split rule for a split, S;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in the left sub-node of node T;
NTR is a number of cases in the right sub-node of node T;
c is a user-selectable variable between 0 and 1.
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10. The system of claim 3, wherein the response variable is a numerical variable and the prediction variable is a categorical variable.
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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 variable and wherein the goodness of split rule is of the form:
-
where Φ
(s) represents a goodness of split rule for a split, s;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in the left sub-node of node T; and
NTR is a number of cases in the right sub-node of node T.
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12. A yield management method, comprising:
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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 further comprising removing prediction variables having more than a predetermined number of missing values, removing prediction variables 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 a model based on the pre-processed data, the model being a decision tree;
modifying the model based on user input; and
analyzing the model using statistical tools to examine one or more key yield factors based on the input data set. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
where Φ
(s) represents a goodness of split rule for a split, s;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in the left sub-node of node T; and
NTR is a number of cases in the right sub-node of node T.
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17. The method of claim 14, wherein the prediction and response variables are numerical.
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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 variable for a particular case is less than or equal to a first predetermined value of the predictor variables or if the value of the predictor variable for the particular case is between the first predetermined value and a second predetermined value of the predictor variables and wherein the goodness of split rule is of the form:
-
where Φ
(S*) represents a goodness of split rule for a split, S*;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in a left sub-node of node T;
NTR is a number of cases in a right sub-node of node T;
c is a user-selectable variable between 0 and 1.
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19. The method of claim 14, wherein the response variable is a categorical variable and the prediction variable is a numerical variable.
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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 variable for a particular case is less than or equal to a first predetermined value of the predictor variables or if the value of the predictor variable for the particular case is between the first predetermined value and a second predetermined value of the predictor variables and wherein the goodness of split rule is of the form:
-
where Φ
(S) represents a goodness of split rule for a split, S;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in the left sub-node of node T;
NTR is a number of cases in the right sub-node of node T;
c is a user-selectable variable between 0 and 1.
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21. The method of claim 14, wherein the response variable is a numerical variable and the prediction variable is a categorical variable.
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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 variable and wherein the goodness of split rule is of the form:
-
where Φ
(s) represents a goodness of split rule for a split, s;
g(T) represents noisiness of a node T;
g(TL) represents noisiness of a left sub-node of node T;
g(TR) represents noisiness of a right sub-node of node T;
NT is a number of cases in node T;
NTL is a number of cases in the left sub-node of node T; and
NTR is a number of cases in the right sub-node of node T.
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Specification