Automated electromagnetic interference filter design

A method comprising:

• receiving, by one or more processors, a plurality of parameters for a filter design, the plurality of parameters including a noise parameter;
• determining a plurality of candidate filter configurations based on at least one of the parameters;
• determining, for each candidate filter configuration of the plurality of candidate filter configurations, based on a trained machine learning model, an estimated electromagnetic interference (EMI) noise associated with the candidate filter configuration;
• ranking the candidate filter configurations based on the estimated EMI noise amount determined as outputs of the machine learning model for each respective candidate filter configuration;
• selecting a subset of highest ranked candidate filter configurations of the candidate filter configurations based on the ranking determined based on the outputs of the machine learning model;
• executing respective simulations of respective candidate filter configurations included in the selected subset of highest ranked candidate filter configurations by executing the respective simulations of the respective candidate filter configurations with a simulation of a system in which the respective candidate filter configurations are intended to be used to determine a simulated EMI noise for the respective candidate filter configurations;
• and
• sending information related to at least one of the respective candidate filter configurations to a computing device.

One or more computer-readable media storing instructions that, when executed by one or more processors, program the one or more processors to perform operations comprising:

• receiving, from a computing device, a plurality of parameters for a filter design, the plurality of parameters including a noise parameter;
• determining a plurality of candidate filter configurations based on at least one of the parameters;
• determining, for each candidate filter configuration of the plurality of candidate filter configurations, based on a trained machine learning model, an estimated electromagnetic interference (EMI) noise associated with the candidate filter configuration;
• ranking the candidate filter configurations based on the estimated EMI noise amount determined as outputs of the machine learning model for each respective candidate filter configuration;
• selecting a subset of highest ranked candidate filter configurations of the candidate filter configurations based on the ranking determined based on the outputs of the machine learning model;
• executing respective simulations of respective candidate filter configurations included in the selected subset of highest ranked candidate filter configurations by executing the respective simulations of the respective candidate filter configurations with a simulation of a system in which the respective candidate filter configurations are intended to be used to determine a simulated EMI noise for the respective candidate filter configurations;
• and
• sending information related to at least one of the respective candidate filter configurations to the computing device.