ChargeSimulatorSolcore

The ChargeSimulatorSolcore is a 1D poisson-drift-diffusion simulator based on the open-source software Solcore, made compatible with the openbandparams database.

Warning

There are currently issues with the solcore simulator, namely:

../_images/charge_transport_1D_interpolation.png

Illustration of the 1D to 2D data interpolation performed with the ChargeSimmulatorSolcore.

class imodulator.ChargeSimulator.ChargeSimulatorSolcore(device, simulation_line, temperature=300.0, bias_start_stop_step=[0, 1, 1])
Each SemiconductorPolygon can have the following charge_transport_kwargs:

  • sol_obp_material: a openbandparams material object,

  • sol_Na: concentration of acceptors in cm^-3,

  • sol_Nd: concentration of donors in cm^-3,

  • sol_electron_minority_lifetime: electron minority lifetime in seconds,

  • sol_hole_minority_lifetime: hole minority lifetime in seconds,

Warning

This solver has a current issue with the ordering of the voltages when it outputs the results. Please visit https://github.com/qpv-research-group/solcore5/issues/294 for more details.

__init__(device, simulation_line, temperature=300.0, bias_start_stop_step=[0, 1, 1])

Initialize the ChargeSimulatorNN.

Parameters:

  • device (PhotonicDevice) – PhotonicDevice instance containing the device geometry and materials.

  • simulation_line (LineString) – LineString defining the simulation line along which to perform 1D simulation.

  • temperature (float (default: 300.0)) – Simulation temperature in Kelvin. Defaults to 300.0.

  • bias_start_stop_step (list (default: [0, 1, 1])) – Voltage sweep [start, stop, steps]. Defaults to [0,1,1].

plot_mesh()

Plot the mesh points along the simulation line.

Returns:

matplotlib figure and axis objects

Return type:

fig, ax

plot_results(V_idx=None, cmap='tab10', log_scale_carriers=True, plot_limits=True)

Plot simulation results in a 2x1 subplot layout.

Parameters:

  • V_idx (list, optional) – Indices of voltages to plot. Defaults to first and last.

  • cmap (str, optional) – Colormap for voltage lines. Defaults to ‘tab10’.

  • log_scale_carriers (bool, optional) – Whether to use log scale for carrier concentrations. Defaults to True.

  • plot_limits (bool, optional) – Whether to plot vertical lines at segment boundaries. Defaults to True.

Returns:

Figure and axes objects

Return type:

tuple

plot_with_simulation_line(color_polygon='black', color_line='green', color_junctions='blue', color_simulation_line='red', fill_polygons=False, linewidth_simulation=2, fig=None, ax=None)

Plot the device polygons with the simulation line overlay.

Parameters:

  • color_polygon (default: 'black') – Color for device polygons

  • color_line (default: 'green') – Color for current calculation lines

  • color_junctions (default: 'blue') – Color for junction regions

  • color_simulation_line (default: 'red') – Color for the simulation line

  • fill_polygons (default: False) – Whether to fill the polygons

  • linewidth_simulation (default: 2) – Line width for simulation line

  • fig (default: None) – Existing figure object (optional)

  • ax (default: None) – Existing axis object (optional)

Returns:

matplotlib figure and axis objects

Return type:

fig, ax

solve_PDD(verbose=False, tol=1e-06, max_iter=100, htp=1, smooth_output=False, savgol_window=15)

Solve the Poisson Drift Diffusion equations for the device.

Warning

The current implementation of the sesame pdd in solcore has a small bug that can cause the outputs to be switched around. Because the solver must first solve the 0V and then it goes to some voltage, the solver does some reordering of the voltages to make sure that the solver converges. However, when storing the voltages corresponding to the outputs in the junction object, it is not saving the correct voltages yet, and for that reason, the outputs can be switched around. For more details go to: https://github.com/qpv-research-group/solcore5/issues/294

This function will update self object with the following attributes:

  • self.V: The potential distribution in the device.

  • self.Ec: The conduction band edge energy distribution in the device.

  • self.Ev: The valence band edge energy distribution in the device.

  • self.Efn: The Fermi level energy distribution in the device.

  • self.Efp: The Fermi level energy distribution in the device.

  • self.N: The electron concentration distribution in the device.

  • self.P: The hole concentration distribution in the device.

  • self.Efield: The electric field distribution in the device.

  • self.mun: The electron mobility distribution in the device.

  • self.mup: The hole mobility distribution in the device.

Parameters:

  • verbose (bool) – Whether to print detailed output.

  • tol (float) – Tolerance for convergence.

  • max_iter (int) – Maximum number of iterations.

  • htp (int) – Number of homotopic Newton loops to perform

  • Note:: (..) – For more details on these arguments please consult solcore.sesame_drift_diffusion.solve_pdd

  • smooth_output (bool) – If True, it will apply a savitzky-golay filter to each of the attributes specified above. The reasoning behind this is to numerical artifacts with big spikes in the charge distributions and electrical fields. For more details please check https://github.com/qpv-research-group/solcore5/discussions/293

  • savgol_window (int) – The window size for the savitzky-golay filter. Default is 15. It should be an odd number.

Returns:

None

transfer_results_to_device(dx=0.05, xmin=-2, xmax=2)

Interpolate 1D simulation data onto a new 2D mesh.

Note

This method for the moment it only interpolates the 1D data onto the horizontal dimension.

Parameters:

  • dx (float, optional) – Step size for new mesh in microns. Defaults to 0.05.

  • xmin (float) – Minimum x value for mesh (required).

  • xmax (float) – Maximum x value for mesh (required).

Returns:

None. Stores interpolators in self.photonicdevice.charge.

References