Noise Modeling and Simulation of Giant Magnetic Impedance (GMI) Magnetic Sensor

Abstract

The detection resolution of a giant magneto-impedance (GMI) sensor is mainly limited by its equivalent input magnetic noise. The noise characteristics of a GMI sensor are evaluated by noise modeling and simulation, which can further optimize the circuit design. This paper first analyzes the noise source of the GMI sensor. It discusses the noise model of the circuit, the output sensitivity model and the modeling process of equivalent input magnetic noise. The noise characteristics of three modules that have the greatest impact on the output noise are then simulated. Finally, the simulation results are verified by experiments. By comparing the simulated noise spectrum curve and the experimental noise spectrum curve, it is demonstrated that the preamplifier and the multiplier contribute the most to the output white noise, and the low-pass filter plays a major role in the output 1/f noise. These modules should be given priority in the optimization of the noise of the conditioning circuit. The above results provide technical support for the practical application of low-noise GMI magnetometers.Sensors 2020, 20, 960 2 of 13 component through impedance fluctuations [12,13]. The intrinsic noise of the GMI component is 2~3 magnitudes [14] lower than that of the conditioning circuit. Above all, the conditioning circuit is the main factor of noise sources [15][16][17], and it is mainly studied in this paper.Conditioning circuit noise is inherent noise inside an electronic system, which is caused by the random motion of the charge carriers. It includes the thermal noise of the resistor [18,19], the shot noise of the PN junction and the 1/f noise [20,21]. For the GMI sensor, the conditioning circuit structure is divided into two parts: the excitation circuit and the detection circuit. The excitation circuit mainly includes an incentive source and voltage-to-current converter [22]. The detection circuit includes a preamplifier, a peak detection circuit and an instrumentation amplifier [23,24]. Each part contains multiple noise sources, and the noise effect contributes differently to the total output noise of the sensor. According to the Fries theorem, in order to achieve the best noise characteristics, the signal-to-noise ratio and the equivalent input noise voltage are generally used to weigh and design the various parts of the system [25,26]. Therefore, by modeling the noise of each module in MATLAB, the dominant noise source is found, allowing further optimization of the dominant noise source and significantly improving the noise characteristics of the conditioning circuit.The work of this paper is divided into three steps: firstly, the modeling idea of the equivalent input magnetic noise model of a GMI sensor is discussed. The output voltage noise model, sensitivity model and equivalent input magnetic noise model are established. Then the noise contribution of each module is computed, and the optimization scheme of the dominant noise source is discussed. Finally, the effectiveness of the noise optimization method is verifie...

Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges

“…This can be achieved in a myriad of ways, including optimizing the composition of the wires or modifying the domain structures with stress, heat treatment, and magnetic field annealing. Further improvements to signal processing can be achieved by carefully engineering circuits [80,81], understanding noise behavior [82], and exploring more complex sensor designs that integrate multiple sensing elements for signal filtering [20][21][22]46,[83][84][85][86][87].…”

Giant Magnetoimpedance Receiver With a Double-Superheterodyne Topology for Magnetic Communication

“…In a harsh wireless communication environment, the frequency for magnetic communication is typically as high as several hundred kHz [11]; accordingly, the receiver is greatly affected by flicker (1/f) noise, inversely proportional to the frequency, which deteriorates its sensitivity. The GMIbased receiver can achieve high sensitivity [12]. The external magnetic signal is up-converted due to the GMI effect and then amplified while remaining less sensitive to 1/f noise resulting from this process.…”