Abstract:
A method for performing multiple simulations for a circuit using a first plurality of samples is provided. The method includes obtaining a model of the circuit based on a result of the simulations, determining a failure rate and a confidence interval of the failure rate for the circuit with the performance model. The method includes determining an importance distribution based on the failure rate for the first plurality of samples, wherein the importance distribution is indicative of a probability that a sample value for the circuit will fail the simulation, selecting a second plurality of samples based on the importance distribution, performing a second set of simulations using the second plurality of samples to reduce the confidence interval of the failure rate. When the confidence interval is larger than a value, obtaining an updated performance model and performing new Monte Carlo simulations with new samples.
Abstract:
Disclosed are method(s), system(s), and article(s) of manufacture for implementing layouts for an electronic design using machine learning, where users re-use patterns of layouts that have been previously implemented, and those previous patterns are applied to create recommendations in new situations. An improved approach to perform cross-validations is provided.
Abstract:
A method for determining mismatch variation of circuit components in a circuit is provided. The method includes determining a mismatch contribution for a specification of an integrated circuit design and displaying a list of components in the circuit design sorted according to the mismatch contribution. The method also includes displaying an adjustable scale for a size of the component, modifying the circuit design according to with the size of the component adjusted according to a user input to the adjustable scale, determining an adjusted mismatch contribution of the component, and displaying in the list of components a modified value of the mismatch contribution, and a modified value of an overall standard deviation for the specification in the circuit design.
Abstract:
A system, method, and computer program product for computing device mismatch variation contributions to circuit performance variation. Embodiments estimate which individual devices in a simulated circuit design have the largest impact on circuit performance, while requiring far fewer simulations than traditional multivariate linear regressions. When the samples exceed the mismatch parameters, a linear model is solved by least squares. Otherwise, a linear model is solved by orthogonal matching pursuit (OMP), and if that solution is too inaccurate then a new mixed method builds a better linear model. If the linear solution is too inaccurate, a full linear and quadratic model is made using OMP to select the most important variables, and the full model is fitted using OMP with selected cross terms. The embodiments summarize the output variance in each device, and rank the mismatch contributions based on the summarized contributions.
Abstract:
A system, method, and computer program product for computing device mismatch variation contributions to circuit performance variation. Embodiments estimate which individual devices in a simulated circuit design have the largest impact on circuit performance, while requiring far fewer simulations than traditional multivariate linear regressions. An ordered metric allocates output variance contributions for each input mismatch parameter in a linear model. The embodiments summarize the output variance in each device, and rank the mismatch contributions based on the summarized contributions. Additional sensitivity analysis can derive a final accurate linear contribution. Embodiments can reduce required simulations by a factor of ten.
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:
Embodiments may include receiving an unplaced layout associated with an electronic circuit design and one or more grouping requirements. Embodiments may further include identifying instances that need to be placed at the unplaced layout and areas of the unplaced layout configured to receive the instances. Embodiments may also analyzing one or more instances that need to be placed at the unplaced layout and the one or more areas of the unplaced layout configured to receive the one or more instances. Embodiments may further include determining a location and an orientation for each of the one or more instances based upon, at least in part, the analyzing. Embodiments may also include generating a placed layout based upon, at least in part, the determined location and orientation for each of the one or more instances. Embodiments may further include during the generation of the placed layout, routing the placed layout.
Abstract:
A method for performing multiple simulations for a circuit using a first plurality of samples is provided. The method includes obtaining a model of the circuit based on a result of the simulations, determining a failure rate and a confidence interval of the failure rate for the circuit with the performance model. The method includes determining an importance distribution based on the failure rate for the first plurality of samples, wherein the importance distribution is indicative of a probability that a sample value for the circuit will fail the simulation, selecting a second plurality of samples based on the importance distribution, performing a second set of simulations using the second plurality of samples to reduce the confidence interval of the failure rate. When the confidence interval is larger than a value, obtaining an updated performance model and performing new Monte Carlo simulations with new samples.
Abstract:
A method for determining the tail performance of an integrated circuit is described. The method includes simulating the integrated circuit over samples to obtain values for circuit specifications and sorting the circuit specifications based on an expected number of samples. The method also includes arranging a sequence of samples from the universe according to a sequence in the group of circuit specifications, simulating the integrated circuit with one of the sequence of samples to obtain at least one circuit specification, removing the at least one circuit specification from the group when it satisfies the stop criterion, and modifying a model for a second circuit specification based on the at least one circuit specification. The computer-implemented method also includes reordering the group of circuit specifications based on the model and determining an integrated circuit performance based on a simulation result for the at least one circuit specification.
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 two stage routing analysis, wherein a first stage analysis is an intra-row routing analysis and a second stage is an inter-row routing analysis. Embodiments may also include generating an optimized routing of the one or more nets and displaying the optimized routing at a graphical user interface.