Performance evaluation of linearization strategies for a directly interfaced thermistor

Abstract

Abstract Sensor linearization is an important aspect for enhancing the efficiencies of measuring systems. Conventional methods use additional circuits and/or software models to achieve linearization. The direct interface technique obviates the requirement for intermediate electronic circuits, including linearization circuits, between sensors and embedded systems. Sensor linearization strategies need to be explored to obtain maximized performances from directly interfaced sensing systems. In this work, the performances of both hardware- and software-based strategies for linearization of directly interfaced thermistor sensors are evaluated. The experimental results show that the hardware-based linearization approach (1P_Shunt, 2P_Shunt) can yield a maximum linearized output range, i.e. from 0 °C to ∼70 °C with <1% full scale span (FSS) nonlinearity error (NLE). The sensitivities in both cases are found to be comparable. In 1P_Shunt, the linearized range is found to be almost independent of the β-values, ranging from 3012 K to 3924 K. A high-speed timer, via minimization of quantization errors, allows a significant reduction in measurement time while maintaining the linearized range. With the artificial neural network based linearization approach, a linearized range up to 100 °C and beyond can be achieved. A shallow network with optimum architecture (1-5-1), with Bayesian regularization and log-sigmoid as an activation function, is found to be sufficient to yield <1% FSS NLE.