Efficient calibration of smart sensors is critical in resource-constrained systems. This work introduces a decomposition-based framework for inverse transfer-function approximation, enabling fine-grained control over accuracy and computational complexity. Using a thermistor circuit as a case study, we evaluate different low-cost implementations of a key nonlinear calibration step. Our results on ARM Cortex-M23 show that exponential LUTs with interpolation strike a strong balance between accuracy and resource usage.