Parametric study of doped injection layers
Doped injection layers are commonly used to lower injection barriers between electrode and organic materials, and to increase charge carrier mobility in injection layers. In these injection layers, a charge transfer between dopand and host molecule generates electron-hole pairs which may separate, leading to free charge carriers. How many dopands become active and how many free charge carriers are generated is determined by a complex interplay of various properties such as absolute transport energy levels (EA/IP), coulomb binding energies and energy disorder.
In this tutorial, we show how to setup two parametric systems in LightForge to study the impact of energy levels and doping concentration on voltage drop along the rest of the device (transport layers and emission zone) and on I-V characteristics to analyze conductivity and injection resistance. For this we set up two devices as illustrated below: One device cosisting of doped HIL and insulator to study voltage drop in the device (left), where the insulator hinders charge transport but experiences the same voltage drop as any other transport/emission layers, and one device consisting of doped HIL and high mobility HTL to study conductivity and injection resistance (right).
|System 1: Device cosisting of doped HIL and insulator to study voltage drop in the device||System 2: Device consisting of doped HIL and high mobility HTL to study conductivity and injection resistance|
In the following, we guide you through the individual tabs of the LightForge WaNo. As the settings for the two systems above differ only in minor aspects, you can follow the same setup guide for both studies, and adapt the settings for system 1 or 2 where indicated.
- device_layout: stack
- set_PBC: automatic
- connect_electrodes: enabled
- particle_types: enable holes, electrons and excitons
- use QP output: disabled
- Override Settings: disabled
- excitonics: no excitons
- Press the "+" button to add two more materials, so we can have Host+Dopand and Insulator (Syste m1) or HTL (System 2). Name these materials accordingly.
- For each material, use "input_mode_transport: Par: eaip,sig,l" and set the following parameters:
- Host: IP=5.5, EA=1.0, sigma=0.15 (for both IP and EA), lambda=0.2 (for both IP and EA)
- Dopant: IP=8, EA=5.0, sigma=0.15 (for both IP and EA), lambda=0.2 (for both IP and EA)
- Insulator (only for system 1): IP=8, EA=1.0, sigma=0.1 (for both IP and EA), lambda=0.2 (for both IP and EA)
- HTL (only for system 1): IP=5.5, EA=1.0, sigma=0.07 (for both IP and EA), lambda=0.2 (for both IP and EA)
- For all materials, set "exciton presets" to "doping"
- morphology_width: 20.0 (feel free to increase this value for better statistics, especially at low doping concentrations. This will however increase runtime)
- layers: Add one layer to create a total of two layers. For both layers, set thickness: 20.0, gsp: 0.0, morphology_input_mode: cubic
- layer 1: press the "+" button in the molecular_species box to add one material. Chose "Host" and "Dopant" for the two materials from the dropdown menue and set concentrations to 0.95 (Host) and 0.05 (Dopant)
- layer 2: Chose Insulator (for System 1) or HTL (for System 2) at a concentration of 1.0
- electrodes: We set the same electrodes at both sides with the following settings:
- electrode_workfunction: -4.5
- coupling_model: parametrized
- electrode_wf_decay_length: 0.1
- electrode_coupling: 0.001
- max neighbors: 26
- transfer_integral_source: Miller-Abrahams
- pair parameters: You need to add pair parameters for all connected pairs in the system, i.e. Host-Host, Host-Dopant, Dopant-Dopant, Host-Insulator/HTL, Dopant-Insulator/HTL, HTL-HTL, to be defined from the dopdown menues "molecule 1" and "molecule 2". For each pair, set the following trasfer_integral_parameters:
- hole_transfer_integrals: wf_decay_length: 0.1, maximum_ti: 0.001
- electron_transfer_integrals: wf_decay_length: 0.1, maximum_ti: 0.001
- Dexter_transfer_integrals: wf_decay_length: 0.1, maximum_ti: 0.0001 (whatch out: one more zero)
- rates: mixed-marcus
- epxilon_material: 4.0
- superexchange: disabled
- check "show advanced" (more settings will appear)
- distance_epsilon: disabled
- screening_length: 1.3 (standard value, will not have an impact)
- doping: enabled
- doping_regeneration_steps: 30
- coulomb_correction: enabled
- coulomb_correction_mode: cutoff
- coulomb_binding_energy: 0.75
- electrode_stack_distance: 0.8
Leave all other parameters as is.
For both systems:
- restart: disabled
- measurement: DC
- Temperature: 300K
- initial holes: 0
- initial electrons: 0
- activate bond damping: disabled
For system 1, use the following computational settings:
- IV fluctuation: 0.1
- max_iterations: 50000 (fifty thousand)
- max_time: 1.0e5 For this system, it is usually sufficient to run a single virtual experiment ("simulations" to 1), as we are only interested in the equilibrated band diagram. However, as energy levels are distributed randomly according to their distribution, fermi level alignment at low doping concentrations may fluctuate from run to run. We therefore recommend to either look at band diagrams for multiple simulations, or increase system width (leading to longer runtime). Make sure to allocate n+1 (n=simulations) processors in the "Resources" tab of SimStack.
For system 2, use the following computational settings:
- IV fluctuation: 0.01
- max_iterations: 10000000 (10Mn)
- max_time: 1.0e5 Current measurements for I-V can be averaged over multiple runs, so here it makes sense to run multiple virtual experiments, which run in parallel on multiple cores of a single node. We therefore recommend to set simulations to 30, and then allocate 31 processors in the "Resources" tab of SimStack.
Results of this study are summarized in this presentation
The results of the search are