Temperature Compensation Algorithm of Tunneling Magnetoresistance Sensors Based on DOA-RBF

The elements in tunneling magnetoresistance (TMR) sensors are composed of the metal material, whose resistivity is affected by temperature. Therefore, zero temperature drift of the Wheatstone bridge sensor is highly dependent on temperature. The consistency of TMR sensors” outputs at different temperatures in practical can be improved through the temperature compensation, reducing the dependence on temperature. A new temperature compensation algorithm for TMR sensors based on DOA-RBF is proposed in this paper. Four specific stages are used to complete the hyperparameter optimization of the RBF neural network. By adjusting the parameters of the Lévy flight function, the iteration speed of hyperparameter optimization process can be improved. It is shown that DOA-RBF has basically converged to the optimal solution after 7 iterations in MATLAB. After compensation, zero temperature coefficient of the sensor is 8.66ppm/
^○
C, and the sensitivity temperature coefficient is 33.6ppm/
^○
C. These two coefficients have an order of magnitude lower than that before compensation. DOA-RBF has the characteristics of shorter optimization time. In this paper, the temperature compensation model is verified by Verilog. The sensitivity temperature coefficient after circuit compensation is kept in the same order of magnitude as that in MATLAB. The method proposed improves the temperature stability of TMR and has good practical application value.