Project Overview
| Role: Lead Developer | Duration: 2024-Present | Organization: CERN EP-R&D |
The Challenge
Optimize 3D columnar silicon sensor geometry for extreme radiation environments (10^16 particles/cm²) targeting the LHCb VELO Upgrade II. Sensors must deliver ~20 ps timing per track while surviving HL-LHC radiation levels.
Technical Approach
- Developed TCAD simulation framework for electric field modeling of 3D columnar electrode architectures
- Built Python-based parameter optimization pipeline sweeping cell size, electrode depth, and doping profiles
- Automated analysis of charge collection efficiency across geometry variants (baseline: CNM 50×50 μm² cells, 280 μm active thickness)
- Validated simulation predictions against testbeam data from 3 SPS campaigns (180 GeV/c pions at H6/H8 beamlines)
- Created visualization tools for electric field distributions and weighting potential maps
Technical Stack
Python TCAD NumPy Matplotlib Git Scientific Computing
Current Progress
- Framework operational and producing results for VELO Upgrade II sensor selection
- Multiple geometry configurations under evaluation, feeding back into CNM fabrication runs
- Simulation results cross-checked with EUDAQ telescope measurements (~5 μm pointing resolution at DUT)
- Collaboration with sensor manufacturers for prototype fabrication
Industry Relevance
Skills directly applicable to:
- Semiconductor R&D: Sensor development and characterization methodologies
- Scientific Computing: Large-scale simulation frameworks
- Optimization: Parameter sweeps and design space exploration
- Data Visualization: Communicating complex technical results