Abstract: Testing data is evaluated by machine learning tools to determine whether to include or exclude chips from further testing.

Abstract: Testing data is evaluated by machine learning tools to determine whether to include or exclude chips from further testing.

Abstract: A model is generated for predicting failures at the wafer production level. Input data from sensors is stored as an initial dataset, then data exhibiting excursions or useless impact is removed from the dataset. The dataset is converted into target features, where the target features are useful in predicting whether a wafer will be normal or not. A trade-off between positive and negative results is selected, and a plurality of predictive models are created.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: A method for predicting yield for a semiconductor process. A particular type of wafer is fabricated to have a first set of features disposed on the wafer, with a wafer map identifying a location for each of the first set of features on the wafer. Data from wafer acceptance tests and circuit probe tests is collected over time for wafers of that particular type as made in a semiconductor fabrication process, and at least one training dataset and a least one validation dataset are created from the collected data. A second set of “engineered” features are created and also incorporated onto the wafer and wafer map. Important features from the first and second sets of features are identified and selected, and using those important features as inputs, a number of different process models are run, with yield as the target. The results of the different models can be combined, for example, statistically.

Abstract: A method including: determining a value of a characteristic of a patterning process or a product thereof, at a current value of a processing parameter; determining whether a termination criterion is met by the value of the characteristic; if the termination criterion is not met, determining a new value of the processing parameter from the current value of the processing parameter and a prior value of the processing parameter, and setting the current value to the new value and repeating the determining steps; and if the termination criterion is met, providing the current value of the processing parameter as an approximation of a value of the processing parameter at which the characteristic has a target value.