Real-time neural network-based thermal stress compensation for pressure sensors in precision localization systems

Abstract

This article presents a real-time Artificial Intelligence-based Reconfigurable Self-Calibration Unit (AI-ReSCU) for piezoresistive MEMS pressure sensors, designed to mitigate long-term drift effects induced by thermal stress. The system integrates a compact and reconfigurable neural network to dynamically estimate and correct sensor inaccuracies with minimal energy and area overhead. The architecture comprises a trigger module for detecting deviations from nominal behavior and a compensation engine driven by a quantized neural network optimized for hardware efficiency. The network processes temporal input windows and operates using 24-bit activations and 1-bit weights, enabling real-time inference with ultra-low power consumption. The fully digital system was prototyped in STMicroelectronics’ BCD8 technology, occupying 0.55 mm² and achieving a dynamic power consumption of 4.46 nW under typical conditions, thanks to extensive resource reuse and clock gating strategies. Offline experimental validation on LPS22HH pressure sensors demonstrated the system’s ability to recover up to 1.6 hPa of drift-induced error with a recovery latency of approximately 50 input samples, while maintaining measurement deviations within ±0.5 hPa across multiple stress scenarios.