Fapplied = 0.0 # (V/m)
# --------------------------------
# REGIONAL SETTINGS FOR SIMULATION
# --------------------------------
# GRID
# For 1D, z-axis is choosen
gridfactor = 0.1 #nm
maxgridpoints = 200000 #for controlling the size
# REGIONS
# Region input is a two-dimensional list input.
# An example:
# Si p-n diode. Firstly lets picturize the regional input.
# | Thickness (nm) | Material | Alloy fraction | Doping(cm^-3) | n or p type |
# Layer 0 | 250.0 | Si | 0 | 1e16 | n |
# Layer 1 | 250.0 | Si | 0 | 1e16 | p |
#
# To input this list in Gallium, we use lists as:
material =[[ 20.0, 'AlGaAs', 0.2, 0, 'n'],
[10.0, 'GaAs', 0, 2e18, 'n'],
[20.0, 'AlGaAs', 0.2, 0, 'n']]
if __name__ == "__main__": #this code allows you to run the input file directly
input_obj = vars()
import aestimo
aestimo.run_aestimo(input_obj)
# QUANTUM
# Total subband number to be calculated for electrons
subnumber_e = 1
# --------------------------------
# REGIONAL SETTINGS FOR SIMULATION
# --------------------------------
# GRID
# For 1D, z-axis is choosen
gridfactor = 0.2 #nm
maxgridpoints = 200000 #for controlling the size
# REGIONS
# Region input is a two-dimensional list input.
# An example:
# Si p-n diode. Firstly lets picturize the regional input.
# | Thickness (nm) | Material | Alloy fraction | Doping(cm^-3) | n or p type |
# Layer 0 | 250.0 | Si | 0 | 1e16 | n |
# Layer 1 | 250.0 | Si | 0 | 1e16 | p |
#
# To input this list in Gallium, we use lists as:
material =[[500.0, 'Si', 0, 1.0e16, 'p'],
[500.0, 'Si', 0, 1.0e16, 'n']]
if __name__ == "__main__": #this code allows you to run the input file directly
input_obj = vars()
import aestimo
aestimo.run_aestimo(input_obj)
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. See ~/COPYING file or http://www.gnu.org/copyleft/gpl.txt .
For the list of contributors, see ~/AUTHORS
Description: This is the main file.
"""
#import matplotlib.pyplot as pl
#import numpy as np
#import time
#import sys
import config
#import aestimo_h as aestimo
import aestimo
# Import from config file
inputfile = __import__(config.inputfilename)
aestimo.logger.info("inputfile is %s" %config.inputfilename)
aestimo.run_aestimo(inputfile)
along with this program. See ~/COPYING file or http://www.gnu.org/copyleft/gpl.txt .
For the list of contributors, see ~/AUTHORS
File Information:
-----------------
This file is one method of running aestimo. Simply define the input file in
the config.py module and run this script. We could also run aestimo.py directly
to achieve the same effect.
Alternatively, many of the example input files show how we can transform them
into scripts that can be run directly to perform the simulations. That approach
also allows us to tailor each simulation even more to our needs.
"""
#import matplotlib.pyplot as pl
#import numpy as np
#import time
#import sys
import config
#import aestimo_eh as aestimo
import aestimo
# Import from config file
inputfile = __import__(config.inputfilename)
aestimo.logger.info("inputfile is %s" % config.inputfilename)
if __name__ == "__main__":
aestimo.run_aestimo(inputfile)