Abstract:
The present disclosure relates to a computer-implemented method for genetic placement of analog and mix-signal circuit components. Embodiments may include receiving an unplaced layout associated with an electronic circuit design and grouping requirements. Embodiments may also include identifying one or more instances that need to be placed in the unplaced layout and areas of the unplaced layout configured to receive the instances. Embodiments may further include analyzing one or more instances that need to be placed in the unplaced layout and the areas of the unplaced layout configured to receive the instances, wherein analyzing is based upon a row-based data structure. Embodiments may also include determining a location and an orientation for each of the one or more instances based upon the genetic algorithm and generating a placed layout based upon the determined location and orientation for each of the instances.
Abstract:
A system, method, and computer program product for efficiently finding the best Monte Carlo simulation samples for use as design corners for all design specifications to substitute for a full circuit design verification. Embodiments calculate a corner target value matching an input variation level by modeling the circuit performance with verified accuracy, estimate the corner based on a response surface model such that the corner has the highest probability density (or extrapolation from the worst sample if the model is inaccurate), and verify and/or adjust the corner by performing a small number of additional simulations. Embodiments also estimate the probability that a design already meets the design specifications at a specified variation level. Composite multimodal and non-Gaussian probability distribution functions enhance model accuracy. The extracted design corners may be of particular utility during circuit design iterations. A potential twenty-fold reduction in overall design specification verification time may be achieved.
Abstract:
A system, method, and computer program product for automatically approximating conventional Monte Carlo statistical device model evaluation for circuit simulation with drastic speed improvements, while preserving significant accuracy. Embodiments enable quick inspection of the effects of process mismatch variations on single devices and even large circuits compared to standard computationally prohibitive Monte Carlo analysis. Statistical device model variation is calculated as if all such variation is due to changes in threshold voltage, even though other physical phenomena are known to contribute. Threshold voltage variation is modeled as a function of statistical variation, device size, and working bias condition. Circuit simulation is faster when the full internal device model parameter set is not rebuilt for every Monte Carlo analysis iteration. Embodiments are compatible with both conventional SPICE and newer Fast SPICE simulations. Circuit designers may capture design sensitivity to manufacture process changes more easily with simplified statistical models.
Abstract:
Embodiments include herein are directed towards a method for electronic circuit design. Embodiments may include receiving, using a processor, an electronic design library including a plurality of design rules. Embodiments may include generating a routing graph, based upon, at least in part, the plurality of design rules, wherein the routing graph is a virtual representation of all of the available routing space for all routing layers associated with an electronic design. Embodiments may further include dynamically updating the routing graph at a graphical user interface, based upon, at least in part, a creation of a routing segment or a via at the graphical user interface.
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:
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 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 method including separating multiple signal waveforms into multiple blocks forming a sequence is provided. Each of the blocks includes at least a portion of each of the multiple signal waveforms. The method includes identifying a shared time portion and a shared signal portion for the signal waveforms within a first block from the multiple blocks and selecting a format for the first block based on a block size of the first block and a block read time of the first block. The method also includes compressing data in the first block based on the shared time portion, the shared signal portion, a preceding block and a subsequent block in the sequence, and storing the first block in a memory based on the format selected for the first block.
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.