Ensuring the stability and precision of voltage references is critical in the domain of automotive electronics, where consistent analog-to-digital conversion is essential. Zener diodes, commonly employed as voltage reference sources, suffer from thermal instability, which can degrade the overall system accuracy. Traditional validation methods are often manual, time-consuming, and susceptible to human error. This paper presents a fully automated test and validation framework that integrates Standard Commands for Programmable Instruments-based control of instrumentation with Serial Peripheral Interface-based communication between embedded microcontrollers. The system is capable of performing comprehensive temperature-based characterization of Zener references. Furthermore, a digital correction algorithm is implemented to mitigate temperature-induced voltage deviations. Curve-fitting techniques are used to model thermal behavior, and Horner’s method is adopted to optimize the computational efficiency of the correction function. The corrected values are validated against actual measurements to confirm improved voltage stability. The results demonstrate the framework’s potential for rapid, accurate, and repeatable validation of reference components in automotive environments, offering significant advancements in both test automation and system reliability. Experimental validation was performed on multiple Zener-based reference sources across a temperature range of -40°C to +125°C. The uncorrected voltage references exhibited thermal drift in the range of ±8-10 mV, corresponding to a stability variation of approximately ±0.12%. After applying the proposed digital correction algorithm, the residual drift was reduced to ±1.5 mV, achieving an improvement factor of nearly 6×. Curve-fitting and Horner’s method-based polynomial evaluation reduced computational complexity by ~40% compared to conventional polynomial implementations, enabling efficient real-time execution on resource-constrained embedded controllers. Overall, the automated framework achieved >95% repeatability across repeated test cycles, while reducing manual intervention time by more than 70% compared to traditional validation methods.
KB et al. (Sun,) studied this question.