  Deep learning has the potential to significantly enhance the spatial
resolution of hybrid detectors for electrons, as demonstrated for electron
microscopy using 200 keV electrons with the M\"ONCH 25 {\mu}m pitch charge
integration pixel detector. The deep learning models have been trained using
both simulation data, which are more straightforward to generate with varied
parameters, and measurement data, which are more cumbersome to acquire due to
the required specialized setup of the electron microscope. However, 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, a Monte Carlo simulation was conducted to model the dynamics
of charge carriers within the silicon sensor, aiming to refine 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 consistencies between the simulation and the
measurements were significantly improved by accounting for the charge
repulsion.
