Temperature Hysteresis Calibration Method of MEMS Accelerometer

TL;DR

This paper introduces a new calibration method to improve MEMS accelerometer accuracy by reducing temperature hysteresis errors without hardware changes.

Contribution

A novel calibration method that corrects MEMS accelerometer temperature hysteresis using a mathematical model based on temperature change rate.

Findings

The calibration method reduces hysteresis errors by up to 63%.

The method improves bias and scale factor repeatability and reduces nonlinearity.

Minor trade-offs in noise characteristics are observed, but hysteresis performance is significantly enhanced.

Abstract

Micro-electromechanical system (MEMS) sensors are widely used in various navigation applications because of their cost-effectiveness, low power consumption, and compact size. However, their performance is often degraded by temperature hysteresis, which arises from internal temperature gradients. This paper presents a calibration method that corrects temperature hysteresis without requiring any additional hardware or modifications to the existing MEMS sensor design. By analyzing the correlation between the external temperature change rate and hysteresis errors, a mathematical calibration model is derived. The method is experimentally validated on MEMS accelerometers, with results showing an up to 63% reduction in hysteresis errors. We further evaluate bias repeatability, scale factor repeatability, nonlinearity, and Allan variance to assess the broader impacts of the calibration. Although minor trade-offs in noise characteristics are observed, the overall hysteresis performance is substantially improved. The proposed approach offers a practical and efficient solution for enhancing MEMS sensor accuracy in dynamic thermal environments.

Figures