Simulation of 3D Sensors

UoM administered thesis: Phd

  • Authors:
  • Ching-Hung Lai

Abstract

The Large Hadron Collider (LHC) at CERN has the highest energy and luminosity in the world. Radiation hardness is then a critical requirement for the inner tracker design. The inner tracker is important for identifying heavy quarks using high spatial precision detectors. Silicon detectors are now the primary technology for this application.3D silicon sensors use a novel technology with penetrating electrodes and have excellent radiation hardness by design. It overcomes the signal loss with a low operation voltage by reducing the collection length compared to the current planar technology used in the ATLAS pixel detector.The ATLAS insertable B-layer (IBL) is an upgrade to improve tracking resolution of the inner tracker and will be installed in 2013. It will be composed of 75\% planar sensors and 25\% 3D sensors in the large-$\eta$ region. It is important to simulate the IBL tracking performance and to have a valid model for 3D sensors.This thesis investigated the experimental data for heavily irradiated planar strip sensors and 3D sensors to develop a device simulator, in which impact ionisation has to be included. The modelling has found that the radiation induced effective doping concentration has two linear regimes with a smaller growth rate at high fluences. This shows the possibility to operate silicon sensors with a higher irradiation level. %Avalanche effects have to be included in the device simulation.The signal efficiency of each pixel is the basis to simulate the whole IBL response. A model and a code were developed to calculate the induced signal from electron-hole pairs generated by the traversing charge particles. This results in a 2D efficiency map used as an input of the 3D digitiser for the Geant4 simulation. This map was adopted by the IBL software team for the whole tracker simulation and has been validated by the test beam data.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
  • Stephen Watts (Supervisor)
Award date31 Dec 2013