Design of dual-layer heater based on genetic algorithm to optimize magnetic field gradient in vapor cell

Abstract

Addressing the constraint of magnetic field gradients in the vapor cell on enhancing the sensitivity of atomic magnetometers, this paper proposed a dual-layer heater design based on genetic algorithms, effectively reduced the magnetic field gradients within the vapor cell. The study analyzed the influence of key parameters of the resistive wire, such as wire width, thickness, and spacing, on magnetic noise generation in the three-dimensional model of the heater. The parameter combinations were then optimized synchronously using genetic algorithms to reduce the magnetic field gradient in the vapor cell region and enhance the magnetic noise self-suppression capability of the heater. The simulation results confirmed that the magnetic field strength in most areas remains below 40 pT, and the magnetic field gradient was well-managed. Additionally, further magnetic field experiments demonstrated that the heater's current-generated magnetic field had a strong self-suppression effect on magnetic noise, as evidenced by the index k value of -0.05. This paper provides convincing technical support and experimental evidence for improving the performance of the SERF atomic magnetometer.

Keywords

• Heater
• Magnetic field self-suppression
• Magnetic field gradient
• Genetic algorithm