A Smart High Accuracy Calibration Algorithm for 3-D Piezoresistive Stress Sensor

Numerical and experimental sensitivity analyses in this paper indicate that the accuracy of a silicon multi-element piezoresistive (PR) stress sensor can be dramatically influenced by the microfabrication non-uniformity and the uncertainties in the values of the PR coefficients and the thermal coefficient of resistance (TCR). The results showed that errors as large as 70% FS or more, in the extracted stress values, may be obtained due to uncertainty of about 2.5% in the values of PR coefficients. This paper aims to evaluate the capabilities of the artificial neural network (ANN) to eliminate the error in stress measurement, due to the fabrication non-uniformity within wafer, wafer-to-wafer, and batch-to-batch, for multi-element PR sensing rosettes. In this paper, sensing chips from two different batches were integrated in building the ANN and testing its performance. The proposed calibration technique employs the neural network fitting Toolbox in MATLAB to generate a two-layer feed-forward network, with sigmoid hidden neurons and linear output neurons. Three different configurations of calibration were designed to test the generalization abilities of the ANN in capturing the in-plane stress components exerted on the silicon chip. The results showed that ANN is capable of accurately predicting the stresses applied to the sensing chip with maximum stress error of 1.5% FS with no need for individual, expensive, and time-consuming calibration process for each sensor.