Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design having an original schematic associated therewith and extracting one or more features for each net from the schematic. Embodiments may include storing one or more resistance or capacitance values for each net and applying the one or more resistance or capacitance values as labels for a machine learning model. Embodiments may also include training the machine learning model using one or more actual values to generate a trained model. Embodiments may further include receiving the trained model to predict parasitics for a stitching engine and generating a stitched schematic.

Abstract: A system, method, and computer program product for predicting mismatch contribution in an electronic environment. Embodiments may include modeling, using a processor, a discrete output mismatch contribution problem using sparse logistic regression to generate a mismatch contribution model and applying a cross-validation approach to increase a complexity of the mismatch contribution model. Embodiments may further include computing one or more mismatch contribution values from the mismatch contribution model and defining at least one sizing constraint or determining a worst case result associated with a sampling process based upon, at least in part, the one or more mismatch contribution values.

Abstract: The present disclosure relates to a computer-implemented method for electronic design. Embodiments may include receiving, using at least one processor, an electronic design schematic and optionally an electronic design layout. Embodiments may further include analyzing the electronic design schematic to determine if one or more required features of a particular circuit structure are present. If the one or more required features are present, embodiments may include analyzing, using a machine learning model, the electronic design schematic to determine if one or more optional features of the particular circuit structure are present.

Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design schematic and an electronic design layout and analyzing, via machine learning, at least one schematic feature from a pair of devices associated with the electronic design schematic. Embodiments may further include determining, based, at least in part, upon the analyzing, whether the pair of devices should be grouped together.

Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design having one or more unoptimized nets. Embodiments may further include applying a genetic algorithm to the electronic design, wherein the genetic algorithm includes a multi-stage routing analysis. A first stage analysis may apply a device-level global routing analysis, a second stage analysis may include an intra-row routing analysis, a third stage may include an inter-row routing analysis, and a fourth stage may include a post-routing optimization analysis. Embodiments may also include generating an optimized routing of the one or more unoptimized nets and displaying the optimized routing at a graphical user interface.

Abstract: The present disclosure relates to a computer-implemented method for electronic design. Embodiments may include receiving, using at least one processor, an electronic design schematic and an electronic design layout and training a model using at least one predictor associated with the electronic design layout. Embodiments may further include obtaining an updated model, based upon, at least in part, the training. Embodiments may also include applying the updated model to a second electronic design schematic or a second electronic design layout, wherein one or more hard constraints or one or more soft constraints or both are created, based upon, at least in part, the model.

Abstract: The present disclosure relates to a computer-implemented method for electronic circuit design. Embodiments may include receiving, using at least one processor, data corresponding to an electronic design schematic. Embodiments may further include analyzing the data to learn one or more device size parameters, a range of parameters, or a matching relationship of parameters based upon, at least in part, the electronic design schematic or the electronic design layout, wherein analyzing occurs without user action.

Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design and determining an objective function associated with the electronic design. Embodiments may further include optimizing the objective function using Bayesian optimization and generating a best hyper-parameter setting based upon, at least in part, the Bayesian optimization.