Symbolic Simulation of Sensor Trimming Algorithms Using Affine Arithmetic

The quality of trimming and compensation algorithms is key to meet the ever-increasing demands on sensor performance. This work presents an approach to effectively develop and validate trimming algorithms based on symbolic simulation with affine arithmetic (AA). Simulations are performed on intervals to cover a complete parameter space at once. This ensures that the algorithm converges to a correct solution in all cases and not only for a concrete testcase. Moreover, the unique deviation symbols of affine forms allow easy root cause analysis if a trim step has failed. This speeds up the development of new algorithms. However, for classical AA, this is limited to first-order deviation terms. We propose an extension that tracks root causes up to second-order effects, i.e. the combination of two deviation sources. The applicability for industrial cases is shown at the example of a binary search algorithm.