Patent ID: 11941337
Assignee: KEYSIGHT TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A system for modeling a nonlinear component for use in circuit design, the system comprising:
at least one processor; and
at least one memory that or stores instructions that, when executed by the at least one processor, causes the at least one processor to:
provide a physical model for modeling a characteristic of the nonlinear component defined by a physical expression for the characteristic, wherein the physical expression comprises a physical nonlinear function depending on at least one variable and a plurality of parameters of the nonlinear component, wherein the plurality of parameters include a selected parameter and remaining parameters;
determine performance data for the characteristic as a function depending on the at least one variable of the physical model;
extract global parameter values for the plurality of parameters based on the performance data corresponding to the characteristic using the physical expression;
extract local parameter values for the selected parameter of the plurality of parameters, while keeping fixed the extracted global parameter values for the remaining parameters of the plurality of parameters, based on the performance data corresponding to the characteristic using the physical expression, wherein the extracted local parameters provide a distribution of the extracted local parameter values for the selected parameter corresponding to different values of the at least one variable;
train an artificial neural network (ANN) function from the extracted local parameter values for the selected parameter depending on the at least one variable;
determine a hybrid model for modeling the characteristic of the nonlinear component defined by a modified physical expression for the characteristic, wherein the modified physical expression comprises the same physical nonlinear function depending on the at least one variable, the remaining parameters, and the trained ANN function depending on the at least one variable in place of the selected parameter; and
design a nonlinear circuit by predicting how the nonlinear component would behave in the nonlinear circuit using the hybrid model or predicting performance characteristics of a modified nonlinear component using simulated characteristics of the hybrid model obtained by modifying the remaining parameter values.