Abstract. In this paper we present a systematic method to determine sets of close to optimal sensor calibration points for a polynomial approximation.For each set of calibration points a polynomial is used to fit the nonlinear sensor response to the calibration reference. The polynomial parameters are calculated using ordinary least square fit. To determine the quality of each calibration, reference sensor data is measured at discrete test conditions. As an error indicator for the quality of a calibration the root mean square deviation between the calibration polynomial and the reference measurement is calculated. The calibration polynomials and the error indicators are calculated for all possible calibration point sets. To find close to optimal calibration point sets, the worst 99 % of the calibration options are dismissed. This results in a multi-dimensional probability distribution of the probably best calibration point sets.In an experiment, barometric MEMS (micro-electromechanical systems) pressure sensors are calibrated using the proposed calibration method at several temperatures and pressures. The framework is applied to a batch of six of each of the following sensor types: Bosch BMP085, Bosch BMP180, and EPCOS T5400. Results indicate which set of calibration points should be chosen to achieve good calibration results.

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Calibration of piezoresistive pressure sensors requires an extensive acquisition of sensor characteristics. Calibration devices have to fulfill high standards of temperature and pressure accuracy and trajectory speed. While pressure control performs quite well, state-of-the-art devices show poor temperature control performance, due to their retarding and nonlinear temperature dynamics. To overcome this problem, the standard industrial PID temperature controller can be replaced by more sophisticated control methods. The vast majority of these control methods, such as model predictive control, require a model of the device's temperature behavior. Black-box models can deliver all necessary information for model-based control applications. This paper focuses on black-box model identification of a calibration device's temperature behavior. A single-input-singleoutput (SISO) model of the nonlinear temperature dynamics is learned by a recurrent neural-network (RNN) with Long-short-term-memory (LSTM) using the resilient propagation (RPROP −) training method. The method is applied to a real calibration fixture, which uses convective cooled Peltier-elements for temperature control. The use of LSTM leads to a timeseries-reproduction error, which is more than 10 times smaller, compared to a RNN having tanh activation functions instead of LSTM.