Abstract:The ultra-low magnetic noise (<10 nT) alkali metal heating technique is a critical component for achieving ultra-high sensitivity in spin-exchange relaxation-free (SERF) atomic magnetometers. In this study, a multi-objective optimization and design method of magnetic field self-suppression heater based on genetic algorithm is proposed. A novel objective function model based on Biot-Savart law is derived, and the optimization objectives include four types (a total of 18 parameters) of parameters, including the length, width, thickness and current direction of the heating wire, in order to obtain the best magnetic self-suppression performance of the heater. Using the finite element analysis method, the magnetic field distribution and temperature distribution in the target region were simulated and analyzed, and the results showed that the heater produced an average magnetic field of 0.02 nT/mA and an average temperature of 180.34°C in the center region of the target region. Experimental tests confirm that the magnetic flux density in the target region falls within the range of 0.13 nT/mA to 0.14 nT/mA, indicating that the heater has a better self-suppressing performance of the magnetic field. This work contributes to further enhancing the performance of atomic magnetometers.
