ANSYS, INC.

Methods and systems for performing timing analysis during the design of a circuit are described. In one embodiment, a simulation system can generate an effective resistance value (or an impedance value based on the effective resistance value) for an instance and use the effective resistance value in a simulation to determine a minimum timing delay for the instance when only the instance switches during such simulations.

Extraction of capacitance values from a design of an electrical circuit can use a set of trained neural networks to generate extracted capacitance values from the circuit using a representation of the Green's function. A method can include the following operations: storing a machine learning model that includes a trained set of one or more neural networks that have been trained to calculate a representation of a Green's function to extract capacitance values from a design of a circuit having a set of conductors; applying, to the machine learning model, a set of inputs representing the set of conductors and their surrounding dielectric materials; encoding the set of inputs through a trained encoder to generate a latent space representation; calculating the values of the Green's function from the latent space representation through a dedicated trained neural network; and calculating the values of the gradient of the Green's function from the latent space representation through another dedicated trained neural network.

A method and a system of generating a smooth spline surface from a quadrilateral mesh representing a physical object, is described. Edge points are added to the quadrilateral mesh to preserve a feature or a boundary. The edge points are added to an edge of the quadrilateral mesh having one or more irregular nodes representing a feature edge of the physical object and/or the edge points are added to an edge of the quadrilateral mesh extending between the feature edge and an irregular node. Knot interval vectors are extracted for each edge point and used to evaluate an irregular element having the edge points to generate the smooth spline surface. The smooth spline surface is highly continuous and can be displayed to model the physical object, modify a design of the physical object, or used directly in a simulation to evaluate the physical object. Other embodiments are also described and claimed.

The disclosed embodiments include a simulation system that guides, through a user interface, a creation of a set of reduced order models (ROMs) with compatible macros to ensure the creation of a set of computations in the simulation is compatible at execution time. The macros can provide a macro based workflow, where the macros evaluate the ROMs in the workflow. The user interface can restrict the set of macros to those that are compatible with a selected set of ROMs. In one embodiment, the set of ROMs can produce a customizable set of outputs using a user defined sequence of ROMs that are associated with corresponding selected macros to simulate a physical system. The set of ROMs and their compatible macros can be encapsulated into a single unit such as a Functional Mock-Up Unit file.

Circuit design techniques can use a trained predictor to predict key dynamic current metrics (such as peak current, peak time, pulse width and total charge) for a gate in a circuit library, where the predictor has been trained over different combinations of different input transition slews and different output fanout models. A dynamic current model solver can be used for a gate in the cell library to derive waveforms (of current versus time) for the different combinations, and a predictor, such as a neural network, can be trained with the outputs from the solver for the different combinations. The trained predictor can be used in a runtime simulation to solve for the dynamic current demand model of the various gates in a circuit design (such as all of the gates in an integrated circuit).

A method for the simulation of a propagation of electromagnetic beams, in particular light beams, is revealed, comprising the following steps of parametric specification of a first beam relative to a first boundary layer, specification of a first material into which the first beam should propagate after the first boundary layer, and determination of a reflection and/or transmission of the first beam relative to the first boundary layer on the basis of the first material and on the basis of a first model, wherein the first model comprises one or more functions which associate one or more reflection and/or transmission parameters with the first beam, and wherein the reflection and/or transmission parameter(s) associated with the first beam via the functions are determined.

A system receives a layout representation of a metasurface, the metasurface including a number of scatterers arranged in a spatial order, each scatterer having one of a number of geometries. The system identifies one or more consecutive ones of the scatterers in the spatial order. The system identifies a sequence representing consecutive ones of the geometries, the sequence being associated with one or more consecutive ones of the scatterers in the spatial order. The system associates a two-dimensional coordinate to the sequence, the two-dimensional coordinate corresponding to a position in the metasurface where the sequence starts. The system generates an output layout file including a reference to the sequence and the associated two-dimensional coordinate.

Systems and methods are provided for generating a radar model for a target object. In embodiments, a target simulation model is received that represents one or more physical aspects of a target object, an environment simulation model is received that represents one or more physical aspects of an environment object, and a target distance parameter is received that identifies a reference distance between the target object and a radar system to be simulated. A simulation model is generated based, at least in part, on the target simulation model, the environment simulation model, and the reference distance, and further based on a target aspect angle that identifies an angular position of the target object in relation to the radar system. Interaction of the radar system with the target object and the environment object is simulated using the simulation model, and results of the simulation are used to generate a range profile for the target object at the target aspect angle, wherein the range profile identifies a radar return strength for the reference distance. The target aspect angle is then incremented, and the operations are repeated until range profiles are generated for the target object at a plurality of target angles amounting to a 360 degree rotation of the target object. The range profiles at the plurality of target angles are then accumulated to generate the radar model for the target object.

A method is disclosed for augmenting the SBR model used in EM field simulation by modeling the specular coherent and diffuse incoherent components of the field scattered by rough surfaces using statistical characteristics of surface roughness. For each projected ray-tube footprint, the magnitude of the coherent radiated field is attenuated with a scalar factor, while the incoherent radiated field is modulated by a random magnitude and phase. Both corrections are based on the statistical characteristics of surface roughness. Multiplying the incoherent field with a randomly generated phase renders it in a mathematically coherent form, which allows the method to generate a statistically viable instance of the total (i.e. coherent plus incoherent) field scattered by a rough surface. The results reproduce the field and power statistics (i.e. mean and variance) observed from direct SBR simulations using an ensemble of explicitly rendered rough surface geometry models, with a significant reduction in computation.

Methods, machine readable media and systems for evaluating, through one or more simulations, the leakage of sensitive data in an integrated circuit, such as cryptographic data or keys, are described. The embodiments can use machine learning models, such as one or more neural networks to generate one or more leakage related scores for each portion in a set of portions of the cryptographic data. In one embodiment, leakage data associated the first set of POIs with one or more neural networks is processed by the one or more neural networks to identify the POIs that leak the most and determine one or more scores for each portion in the set of portions of the cryptographic data.