Uncertainty estimation for a position reconstruction of semiconductor components on a wafer

A method for estimating an uncertainty of an assignment rule that assigns first variables from a first set of first variables to second variables from a second set of second variables, comprising the following steps:

• providing a machine learning system, which is trained, based on the assignment rule, to assign the first variables to the second variables according to the assignment rule;
• ascertaining inaccuracies of the machine learning system, wherein the inaccuracies are ascertained using a difference between second variables predicted by the machine learning system, the second variables being predicted depending on the first variables, and the second variables assigned to the first variables according to the assignment rule;
• ascertaining a covariance matrix depending on the ascertained inaccuracies;
• ascertaining a likelihood matrix of the uncertainties of the assignment rule, wherein the ascertained inaccuracies are grouped into a vector, wherein the vector, transposed, is multiplied by the covariance matrix, inverted, and a result thereof is multiplied by the vector;
• and
• normalizing the likelihood matrix.

A device configured to estimate an uncertainty of an assignment rule that assigns first variables from a first set of first variables to second variables from a second set of second variables, the device configured to:

• provide a machine learning system, which is trained, based on the assignment rule, to assign the first variables to the second variables according to the assignment rule;
• ascertain inaccuracies of the machine learning system, wherein the inaccuracies are ascertained using a difference between second variables predicted by the machine learning system, the second variables being predicted depending on the first variables, and the second variables assigned to the first variables according to the assignment rule;
• ascertain a covariance matrix depending on the ascertained inaccuracies;
• ascertain a likelihood matrix of the uncertainties of the assignment rule, wherein the ascertained inaccuracies are grouped into a vector, wherein the vector, transposed, is multiplied by the covariance matrix, inverted, and a result thereof is multiplied by the vector;
• and
• normalize the likelihood matrix.

A non-transitory machine-readable storage medium on which is stored a computer program for estimating an uncertainty of an assignment rule that assigns first variables from a first set of first variables to second variables from a second set of second variables, the computer program, when executed by a computer, causing the computer to perform the following steps:

• providing a machine learning system, which is trained, based on the assignment rule, to assign the first variables to the second variables according to the assignment rule;
• ascertaining inaccuracies of the machine learning system, wherein the inaccuracies are ascertained using a difference between second variables predicted by the machine learning system, the second variables being predicted depending on the first variables, and the second variables assigned to the first variables according to the assignment rule;
• ascertaining a covariance matrix depending on the ascertained inaccuracies;
• ascertaining a likelihood matrix of the uncertainties of the assignment rule, wherein the ascertained inaccuracies are grouped into a vector, wherein the vector, transposed, is multiplied by the covariance matrix, inverted, and a result thereof is multiplied by the vector;
• and
• normalizing the likelihood matrix.