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 computer-implemented method determines a quality state of a wafer. The method includes providing at least three process control monitoring metrics of the wafer. Each process control monitoring metric is collected on the wafer at a different process control monitoring coordinate. The method further includes inputting the at least three process control monitoring metrics and the different process control monitoring coordinates of the process control monitoring metrics into at least one machine learning algorithm, and outputting at least three approximated wafer level test values by the at least one machine learning algorithm. The method also includes determining the quality state of the wafer based on the at least three approximated wafer level test values.

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.