Capacitance extraction systems based on machine learning models

Abstract

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.