Optimizing Charge Transport Simulation for Hybrid Pixel Detectors

TL;DR

This paper improves the simulation of charge transport in hybrid pixel detectors by refining models with Monte Carlo simulations, leading to better alignment with experimental measurements and enhanced detector resolution.

Contribution

It introduces an optimized charge transport simulation model that incorporates charge repulsion, significantly improving agreement with measurements.

Findings

Enhanced simulation accuracy with charge repulsion modeling

Improved spectral output matching between simulation and measurement

Better resolution in deep learning models for detector data

Abstract

To enhance the spatial resolution of the M\"ONCH 25 \textmu m pitch hybrid pixel detector, deep learning models have been trained using both simulation and measurement data. Challenges arise when comparing simulation-based deep learning models to measurement-based models for electrons, as the spatial resolution achieved through simulations is notably inferior to that from measurements. Discrepancies are also observed when directly comparing X-ray simulations with measurements, particularly in the spectral output of single pixels. These observations collectively suggest that current simulations require optimization. To address this, the dynamics of charge carriers within the silicon sensor have been studied using Monte Carlo simulations, aiming to refine the charge transport modeling. The simulation encompasses the initial generation of the charge cloud, charge cloud drift, charge diffusion and repulsion, and electronic noise. The simulation results were validated with measurements from the M\"ONCH detector for X-rays, and the agreement between measurements and simulations was significantly improved by accounting for the charge repulsion.