Speaker
Description
With the High Luminosity LHC (HL-LHC) upgrade scheduled for 2030, a major upgrade of the LHCb experiment is planned to adapt to the harsh environment expected with the increased pile-up, as the number of interactions per bunch crossing will increase eight-fold. At the Upstream (UP) and MightyTracker tracking stations, Monolithic Active Pixel Sensors (MAPS) have been chosen for their high resistivity to radiation and their small pixel sizes. This high granularity and the higher particle density at HL-LHC will considerably inflate the data rate generated by these detectors. To fulfill LHCb’s design requirements, a data reduction method must be implemented directly at the sensor level.
In this work, we discuss the implementation of a demonstrator for an improved readout architecture with a lossless data format for pixel tracking detectors subject to high particle rates. This new architecture introduces a timestamp sharing stage, allowing our data format to remove the high redundancy of this information. Our data and readout simulations for UP in LHCb’s Upgrade II show that our method can achieve up to 36% compression rates compared to the best currently available solutions. This will allow us to meet the challenging data throughput conditions at UP in the Run 5 of LHCb, where particle rates can go up to 80 MHz/cm$^2$. The design can therefore allow for readout from 1 to 7 links, depending on the hitrate.
Our designed architecture prototype targets the LF150 technology node for submission in June 2026. The chip, named BIG, for BxId Grouping, should respect the design requirements of UP. Its surface will be around 4 mm$^2$ with an optimized power usage for a datapath representing our complete architecture.