Invertible Neural Networks for Design of Broadband Active Mixers

In this work, we present the invertible neural network for predicting the posterior distributions of the design space of broadband active mixers with RF from 100 MHz to 10 GHz. This invertible method gives a fast and accurate model when investigating crucial properties of active mixers such as conversion gain and noise figure. Our results show that the response generated by the invertible neural network model has close correlation with the output response from the circuit simulator.