Abstract: A method, product and system for Automatic Optical Inspection (AOI) using hybrid imaging system, The method comprises obtaining a prediction model that is configured to predict enhanced-quality images of products based on a low-quality images of the products, wherein the prediction model is generated based images obtained by a dual-scanning system comprising a low-quality scanning system and a high-quality scanning system. Based on a low-quality image of the product that is captured using the low-quality scanning system and using the prediction model, an enhanced-quality image of the product is predicted and utilized for defects detection.

Abstract: A method includes, receiving a layout design of at least part of an electronic module, the design specifying at least (i) an electronic device coupled to at least a substrate, and (ii) an electrical trace that is connected to the electronic device and has a designed route. A digital input, which represents at least part of an actual electronic module that was manufactured in accordance with the layout design but without at least a portion of the electrical trace, is received. An error in coupling the electronic device to the substrate, relative to the layout design, is estimated based on the digital input. An actual route that corrects the estimated error, is calculated for at least the portion of the electrical trace. At least the portion of the electrical trace is formed on the substrate of the actual electronic module, along the actual route instead of the designed route.

Abstract: An Artificial Intelligence system, an apparatus and, a computer program product and a method for automatic improvement of artificial intelligence classification models. A model-performance measurement of the classification model is iteratively improved by at least a predetermined target goal in each iteration. The iterative improvement comprises generating a hypotheses graph for improving the classification model, based on a list of hypotheses and scores thereof. Each hypothesis relates to a strategy for potentially improving the classification model, and is associated with a score indicating a likelihood that an application thereof improves the model-performance measurement. Each node of the hypotheses graph comprises a hypothesis of the list of hypotheses.

Abstract: A method includes, receiving a layout design of at least part of an electronic module, the design specifying at least (i) an electronic device coupled to at least a substrate, and (ii) an electrical trace that is connected to the electronic device and has a designed route. A digital input, which represents at least part of an actual electronic module that was manufactured in accordance with the layout design but without at least a portion of the electrical trace, is received. An error in coupling the electronic device to the substrate, relative to the layout design, is estimated based on the digital input. An actual route that corrects the estimated error, is calculated for at least the portion of the electrical trace. At least the portion of the electrical trace is formed on the substrate of the actual electronic module, along the actual route instead of the designed route.

Abstract: A method, a computerized apparatus and a computer program product for adaptive learning for image classification. The method comprises applying a set of classification models on a calibration dataset and on a production dataset, and calculating disagreement measurements over the predictions thereof on each dataset. Based on similarity measurement between the disagreement measurement of the calibration and the production datasets, being below a predetermined threshold, a data drift is indicated in the production dataset. The method further comprises determining a training dataset for training a classification model for the production dataset. The training dataset is selected over a plurality of sets of images ordered according to time intervals in which images therein are obtained. The selection is performed based on weights determined for the plurality of sets.

Abstract: An Artificial Intelligence system, an apparatus and, a computer program product and a method for automatic improvement of artificial intelligence classification models. A model-performance measurement of the classification model is iteratively improved by at least a predetermined target goal in each iteration. The iterative improvement comprises generating a hypotheses graph for improving the classification model, based on a list of hypotheses and scores thereof. Each hypothesis relates to a strategy for potentially improving the classification model, and is associated with a score indicating a likelihood that an application thereof improves the model-performance measurement. Each node of the hypotheses graph comprises a hypothesis of the list of hypotheses.