A Method for Efficient Localization of Magnetic Field Sources Excited by Execution of Instructions in a Processor

This paper proposes a method for efficient identification of instruction-dependent sources on a printed circuit board (PCB) by localizing magnetic field sources from a limited number of measurements around the PCB. We first excite the processor by generating an artificial leakage signal at a specific frequency that is directly related to processor instructions. Then, we collect all three components of the magnetic field, but only at locations around the edge of the board. Furthermore, we model these magnetic field sources and then solve a forward–backward optimization problem using the model and measured data to identify the locations of the magnetic field sources, the magnitudes of the moments, and their orientations. The localization results are first verified using simulations, then tested when noise is added to the simulation results, and finally verified against measurements on field-programmable gate array (FPGA) and internet of things (IoT) development boards. The results show that the number of strong magnetic field sources on a board depends on the instructions used to excite the board. Furthermore, the results show that the proposed localization algorithm can accurately identify those sources, regardless of the frequency at which the measurements are conducted and the instruction pairs that are executed. Finally, the proposed method can significantly reduce the number of measurement points and the time needed to identify magnetic field sources on a PCB.