Improving sensor output characteristics using small adaptive circuits

This work studies the application of a mixed-mode electronic neural network to improve the output of nonlinear sensors which show behaviour variations for different samples. We present an analog current-based neuron model with digital weights, showing its architecture and features. Modifying the algorithm used in off-chip weight fitting main differences of the electronic architecture, compared to the ideal model, is compensated. A small neural network based on the proposed architecture is applied to improve the output of NTC thermistors and GMR sensors, showing good results. Circuit complexity and performance make these systems suitable to be implemented as sensor on-chip compensation modules.