  Timing systems based on Analog-to-Digital Converters are widely used in the
design of previous high energy physics detectors. In this paper, we propose a
new method based on deep learning to extract the time information from a finite
set of ADC samples. Firstly, a quantitative analysis of the traditional curve
fitting method regarding three kinds of variations (long-term drift, short-term
change and random noise) is presented with simulation illustrations. Next, a
comparative study between curve fitting and the neural networks is made to
demonstrate the potential of deep learning in this problem. Simulations show
that the dedicated network architecture can greatly suppress the noise RMS and
improve timing resolution in non-ideal conditions. Finally, experiments are
performed with the ALICE PHOS FEE card. The performance of our method is more
than 20% better than curve fitting in the experimental condition.
