Abstract: A method for providing wafer test data of at least one wafer with semiconductor chips, in particular for providing a training data set for a machine learning algorithm for anomaly detection, comprising receiving a set of measured variables from the semiconductor chips of the wafer, defining sub-areas on the wafer, each of which comprises a plurality of semiconductor chips of the wafer for which measured variables have been received, and outputting a reduced set of measured variables compared to the received set of measured variables, which comprises only the measured variables of subsets of the semiconductor chips of the respective sub-areas.

Abstract: A method is for detecting anomalies on a surface of an object and includes creating a depth profile of the surface of the object, and pre-processing the depth profile by approximating a shape along a spatial dimension and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile. The method further includes detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile. The machine learning algorithm is trained in order to detect anomalies in depth profiles.

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.

Abstract: An information processing device is configured to acquire prediction information of a weather condition outside a plastic greenhouse and cultivation information of produce inside the plastic greenhouse and predict an environmental condition inside the plastic greenhouse based on the prediction information of the weather condition and the cultivation information. Cultivation information includes at least one item of information from among the type of produce, a cultivation amount, a growth state, and a cultivation ground. A prediction model may be generated with machine learning.