Radio detection of neutrinos remains the most promising technique for the detection of UHE neutrinos.
Construction of large-scale radio-neutrino detectors, however, is limited by logistics;
thus, optimization of the detector stations is the only way to enhance the science reach of future radio detectors.
Improving the trigger efficiency for faint signals is thus crucial.
A complete digital readout chain for the antennas enables the implementation of the trigger in the field-programmable gate array (FPGA) logic,
allowing for more flexible and advanced trigger decisions based on neural networks.
In a first approach, a conventional pre-trigger reduces the data rate to 10 kHz
followed by a second-stage CNN-based neural network that further reduces the trigger rate to 1 Hz.
The performance can be further improved by a continuously running CNN-based neural network
that operates directly on the raw data, but requires a more complex model to run on the FPGA.
Simulation studies suggest an enhancement in the neutrino detection rate by up to a factor of two,
translating into a factor-of-two improvement of most science objectives.
We set up evaluation boards in the lab and performed commissioning runs. The setup allows to quantify the operation, power consumption, background rejection, and signal efficiency.