Error compensation of A/D converters using neural networks

This paper describes a new technique for compensating errors in analog-to-digital converters (ADC's). It can be considered an improvement of the phase plane compensation technique: the idea is to exploit the generalization capabilities of Artificial Neural Networks (ANN's) to reduce the large number of experiments required. The ANN is built and set up in a simulation environment using an ADC behavioral model, whose errors can be fixed to known values. It is thus possible to simulate a set of ADC's with very different performances, thereby enabling the usefulness of the proposed approach to be investigated in very different working conditions. The results were analyzed by comparing the behaviour of uncompensated and compensated ADC outputs.