Probabilistic Characterization and Machine Learning-Based Modeling of Conducted Emissions of Programmable Microcontrollers

To evaluate the system-level electromagnetic emissions of an automobile, in a simulation environment, individual sources of electromagnetic interference (EMI) must be modeled. Modeling the emission behavior of electronic controller units (ECUs) in automobiles requires the characterization of the main microcontroller as an emission source. The conducted emissions (CEs) of the microcontroller depend on the programs running inside the core and the functional blocks utilized for those programs. We propose a measurement-based stochastic model to characterize such program dependency on the conducted emissions of a microcontroller. From board-level CE measurements and the functional blocks utilized for the programs, we obtain the upper and lower bounds of the emissions within which the microcontroller’s conducted emissions are expected to lie with a 95% confidence interval. Further, we propose a method to estimate the emission peaks of the microcontroller when a new program runs in its core with a ±4 dB average error. The functional blocks used for this analysis involve the RAM, timers, and GPIOs. The method works satisfactorily at frequencies up to 500 MHz and is tested on an STM32 general-purpose microcontroller board.