Abstract: A method is for teaching a machine learning system to predict failure probabilities of chips on a wafer. The method is based on a prediction of final test yields depending on wafer-level test measurements by the machine learning system. The machine learning system predicts the failure probabilities of the chips depending on the wafer-level test measurements, and the predicted failure probabilities are aggregated to the predicted final test yields. The machine learning system is trained, such that a math difference between predicted final test yields and final test yields from a training data set is minimized. Differences between the predicted final test yields and final test yields from a training data set are minimized.

Abstract: 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. The method includes: ascertaining inaccuracies of a machine learning system trained with the assignment rule, wherein the inaccuracies are ascertained by means of a difference between the second variables predicted by the machine learning system 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 and normalizing the likelihood matrix by dividing the value of the likelihood matrix by the corresponding column sum.

Abstract: A computer-implemented method for optimizing a detection threshold of a prediction model used to determine an anomaly of a component is disclosed. The detection threshold indicates the criterion above which the prediction model classifies a component as anomalous.

Abstract: A method is disclosed for determining correlations between first measurements and second measurements after respectively different production steps for optimizing the production steps. The method includes multiple repetition of the following steps: drawing random samples from the set of measurements and calculating a correlation matrix between the first and the second measurements contained in the randomly drawn sample. After the repetitions have been completed, a mean correlation matrix is determined over the correlation matrices and a standard variance matrix of the correlation matrices. Then follows a determination of significance values based on a division of absolute value of the mean correlation matrix element by element with the absolute values of the standard variance matrix. Depending on the significance values, one of the production steps can be adjusted.

Abstract: A method determines an assignment rule in order to combine test results from different tests of the same semiconductor device. The method includes fitting a model, such as a linear regression model, using the model to predict the test data, calculating a cost matrix based on the predictions, and applying the Hungarian method to the cost matrix to obtain a new assignment rule and repeating these steps multiple times.