diff --git a/.github/workflows/PyPi-publish.yml b/.github/workflows/PyPi-publish.yml new file mode 100644 index 0000000..db52a28 --- /dev/null +++ b/.github/workflows/PyPi-publish.yml @@ -0,0 +1,84 @@ +# This workflow will upload a Python Package to PyPI when a release is created +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package to PyPI and TestPyPI + +on: push + +jobs: + release-build: + name: Build distribution + runs-on: ubuntu-latest + + steps: + - name: Check out repository + uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ["3.12"] + - name: Install pypa build dependencies + run: | + python -m pip install --upgrade pip + python -m pip install build + - name: Build package (binary wheel and a source tarball) + run: python -m build + - name: Store the distribution package + uses: actions/upload-artifact@v4 + with: + name: release-dists + path: dist/ + + publish-to-pypi: + name: Publish package to PyPI + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + runs-on: ubuntu-latest + needs: + - release-build + permissions: + id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + # Dedicated environments with protections for publishing are strongly recommended. + # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules + environment: + name: pypi + url: https://pypi.org/p/mobilitypy + + steps: + - name: Retrieve release distribution + uses: actions/download-artifact@v4 + with: + name: release-dists + path: dist/ + - name: Publish release distribution to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist/ + + publish-to-testpypi: + name: Publish package to TestPyPI + needs: + - release-build + runs-on: ubuntu-latest + + environment: + name: testpypi + url: https://test.pypi.org/p/mobilitypy + + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + + steps: + - name: Download all the dists + uses: actions/download-artifact@v6 + with: + name: release-dists + path: dist/ + - name: Publish distribution to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ diff --git a/README.md b/README.md index e16fe12..9865f30 100644 --- a/README.md +++ b/README.md @@ -115,7 +115,6 @@ __Bibliography file:__ Here is the [bibliography file](https://github.com/Semico ## Version release -__Latest release: v1.0.1__ Chekout out [version release history here](https://github.com/SemiconductorTransport/mobilitypy/wiki/Brief-release-history) for the full list of updates and upgrades. diff --git a/mobilitypy/__init__.py b/mobilitypy/__init__.py index a22e251..d86367b 100644 --- a/mobilitypy/__init__.py +++ b/mobilitypy/__init__.py @@ -1,7 +1,6 @@ -__version__ = "1.0.1" - -from .mobility import AlloyParams, Mobility2DCarrier, Plottings +from .mobility import AlloyParams, Mobility2DCarrier, Mobility3DCarrier, Plottings from .utilities._quasi3d_plot_fns import PlotQuasi3DFuns ## ============================================================================== -__all__ = ['AlloyParams', 'Mobility2DCarrier', 'Plottings', 'PlotQuasi3DFuns'] +__all__ = ['AlloyParams', 'Mobility2DCarrier', 'Mobility3DCarrier', + 'Plottings', 'PlotQuasi3DFuns'] diff --git a/mobilitypy/mobility.py b/mobilitypy/mobility.py index f632618..458ac2e 100755 --- a/mobilitypy/mobility.py +++ b/mobilitypy/mobility.py @@ -1,4 +1,4 @@ -from .src import _Mobility2DCarrier, _AlloyParams +from .src import _AlloyParams, _MobilityCarrier, _Mobility2DCarrier, _Mobility3DCarrier from .utilities import _plot_mobilities import numpy as np @@ -46,7 +46,7 @@ def get_alloy_params(self, system='ternary', compositions=None, binaries=['AlN', _AlloyParams.__init__(self, compositions=compositions, binaries=binaries, alloy=alloy) return self._get_alloy_params(system=system) -class Mobility2DCarrier(_Mobility2DCarrier): +class Mobility2DCarrier(_MobilityCarrier, _Mobility2DCarrier): """ The functions in this class calculates the mobility of 2D carrier gas. The mobility models are implemented based on the following references. @@ -61,7 +61,9 @@ class Mobility2DCarrier(_Mobility2DCarrier): heterostructures with varied Al content. Sci. China Ser. F-Inf. Sci. 51, 780–789 (2008). https://doi.org/10.1007/s11432-008-0056-7 - Ref-3: Mondal et. al., TBA + Ref-3: Mondal et. al., Interplay of carrier density and mobility in Al-rich (Al,Ga)N-channel HEMTs: + Impact on high-power device performance potential. APL Electronic Devices 1, 026117 (2025) + https://doi.org/10.1063/5.0277051 """ def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', system='ternary', psedomorphic_strain=False, substrate=None, @@ -89,7 +91,7 @@ def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', psedomorphic_strain : bool, optional Whether to consider pseudomorphic strain. The default is False. - substrate : string or float (in Angstrom), optional + substrate : string or float, optional (unit: Angstrom) The substrate name (if string, warning: the name should be in the database) or the substrate in-plane lattice parameter (if float, Angstrom unit). The default is None. Error will be raised if substrate=None and @@ -102,9 +104,9 @@ def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', for zincblende use 'ZB' or 'zb'. for diamond use 'DM' or 'dm'. The default is 'WZ'. - eps_n_2d : float, optional + eps_n_2d : float, optional (unit: nm^-2) Carrier density below eps_n_2d will be considered as zero. - The default is 1e-10. + The default is 1e-10 nm^-2 == 1e4 cm^-2. print_log : string, optional => ['high','medium','low', None] Determines the level of log to be printed. The default is None. @@ -115,10 +117,11 @@ def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', """ if (psedomorphic_strain == True) and (substrate is None): raise ValueError('substrate tag can not be None when psedomorphic_strain=True.') - _Mobility2DCarrier.__init__(self, compositions=compositions, binaries=binaries, - alloy=alloy, system=system, psedomorphic_strain=psedomorphic_strain, - substrate=substrate,alloy_type=alloy_type, - print_log=print_log, eps_n_2d=eps_n_2d) + _MobilityCarrier.__init__(self, compositions=compositions, binaries=binaries, + alloy=alloy, system=system, psedomorphic_strain=psedomorphic_strain, + substrate=substrate,alloy_type=alloy_type, + print_log=print_log, eps_n=eps_n_2d) + _Mobility2DCarrier.__init__(self) def calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, n_dis=1, f_dis=0.1, T=300, alloy_disordered_effect:bool=False, @@ -146,7 +149,9 @@ def calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, n_di heterostructures with varied Al content. Sci. China Ser. F-Inf. Sci. 51, 780–789 (2008). https://doi.org/10.1007/s11432-008-0056-7 - Ref-3: Mondal et. al., TBA + Ref-3: Mondal et. al., Interplay of carrier density and mobility in Al-rich (Al,Ga)N-channel HEMTs: + Impact on high-power device performance potential. APL Electronic Devices 1, 026117 (2025) + https://doi.org/10.1063/5.0277051 The considered scattering mechanism are: Interface roughness mediated (IRF) @@ -158,33 +163,33 @@ def calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, n_di Polar optical phonon (POP) Units: - c_lattice => in nm - a_lattice => in nm - sc_potential => in eV - n_2d => in nm^-2 - rms_roughness => nm - corr_len => nm - n_dis => nm^-2 - f_dis => unit less - E_pop => eV + c_lattice => in nm + a_lattice => in nm + sc_potential => in eV + n_2d => in nm^-2 + rms_roughness => nm + corr_len => nm + n_dis => nm^-2 + f_dis => unit less + E_pop => eV Parameters ---------- - n_2d : 1D float array or float (nm^-2) + n_2d : 1D float array or float, optional (unit: nm^-2) Array containing carrier density data for compositions. This can be a single number as well. Then all compositions will have same carrier - density. - rms_roughness : float, optional (nm) + density. The default is 0.1. + rms_roughness : float, optional (unit: nm) Interface root-mean-squared roughness for interface-roughness scattering contribution. The default is 0.1. - corr_len : float, optional (nm) + corr_len : float, optional (unit: nm) Correlation length of interface roughness. The default is 1. - n_dis : float, optional (nm^-2) + n_dis : float, optional (unit: nm^-2) Threading dislocation density. The default is 1. - f_dis : float, optional + f_dis : float, optional (unit: unitless) Fraction of dislocation that contributes in scattering. The default is 0.1. - T : float, optional (K) + T : float, optional (unit: K) Temperature at which mobility calculations will be done. The default is 300K. alloy_disordered_effect : bool, optional @@ -227,14 +232,12 @@ def calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, n_di Returns ------- - pandas dataframe with compositions and mobility columns. + pandas dataframe with compositions and mobility (unit: cm^2 V^-1 S^-1) columns. Total (or individual contributions) sheet mobility. If return_sc_rates=True, then scattering rates are also returned. """ - ''' - - ''' + self.alloy_disordered_effect_=alloy_disordered_effect self.interface_roughness_effect_=interface_roughness_effect self.dislocation_effect_=dislocation_effect @@ -264,7 +267,7 @@ def sc_rate_2_mobility(mstar0_by_e, inverse_scattering): Returns ------- - float/array + float/array (unit: cm^2 V^-1 S^-1) Total (or individual contributions) sheet mobility. """ @@ -293,16 +296,16 @@ def calculate_sheet_resitance(self, n_2d, mobility): Parameters ---------- - n_2d : 1D float array (nm^-2) + n_2d : 1D float array (unit: nm^-2) Array containing carrier density data for compositions. This can be a single number as well. Then all compositions will have same carrier density. - mobility : 1D float array (cm^2 V^-1 s^-1) + mobility : 1D float array (unit: cm^2 V^-1 s^-1) Array containing mobility data for compositions. Returns ------- - 1D float array (ohm/square) + 1D float array (unit: ohm/square) Sheet resistance for compositions. """ @@ -339,13 +342,13 @@ def calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, Parameters ---------- - n_2d : 1D float array (nm^-2) + n_2d : 1D float array (unit: nm^-2) Array containing carrier density data for compositions. This can be a single number as well. Then all compositions will have same carrier density. - mobility : 1D float array (cm^2 V^-1 s^-1) + mobility : 1D float array (unit: cm^2 V^-1 s^-1) Array containing mobility data for compositions. - temp : float, optional (K) + temp : float, optional (unit: K) Temperature for band gap correction. The default is 300K. mode : str, optional (['LFOM']) The figure-of-merit name. The default is 'LFOM'. @@ -358,7 +361,7 @@ def calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, Returns ------- - 1D float array (MW/cm^2) + 1D float array (unit: MW/cm^2) Figure-of-merit. """ @@ -366,7 +369,187 @@ def calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, T_corect_bandgap=T_corect_bandgap, direct_bandgap=direct_bandgap, indirect_bandgap=indirect_bandgap) + +class Mobility3DCarrier(_MobilityCarrier, _Mobility3DCarrier): + """ + The functions in this class calculates the mobility of 3D carrier gas. + The mobility models are implemented based on the following references. + + Note: Some of the equations in the references has prining mistakes. The mistakes + are corrected in our implementation. + + Ref-1: Rajan et al., Appl. Phys. Lett. 88, 042103 (2006) => alloy disorder, polar optical phonon + Ref-2: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) => Dislocation + Ref-3: Debdeep Jena's thesis, Chapter-6 APPENDIX, Sec. Three-dimensional carriers => Acoustic phonon + + """ + + def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', + system='ternary', psedomorphic_strain=False, substrate=None, + alloy_type='WZ', eps_n_3d=1e-14, print_log=None): + """ + Initialization function of the class Mobility3DCarrier. + + Parameters + ---------- + compositions : 1D array of float, optional + The alloy mole fractions. E.g. x values in Si_xGe_1-x. The default is None. + If None, a composition array is generated using `np.linspace(start=0.01, end=0.99, num=101)`. + binaries : list of strings (case sensitive), optional + Name of the corresponding binaries of requested alloy. They should + match the names in database. All implemented materials name list + can be found in the README. + The default is ['AlN', 'GaN']. + alloy : string (case sensitive), optional + The alloy name. The name should match the name in database. All + implemented materials name list can be found in the README. Case sensitive. + The default is 'AlGaN'. + system : string (case sensitive), optional + Type of the alloy. E.g. 'ternary'. + The default is 'ternary'. + psedomorphic_strain : bool, optional + Whether to consider pseudomorphic strain. + The default is False. + substrate : string or float, optional (unit: Angstrom) + The substrate name (if string, warning: the name should be in the database) + or the substrate in-plane lattice parameter (if float, Angstrom unit). + The default is None. Error will be raised if substrate=None and + psedomorphic_strain=True. + alloy_type : str, optional (case insensitive) + The crystal type of alloy. This will be considered when calculating + parameters like Poisson ratio etc. + Use following abbreviation name: + for wurtzite use 'WZ' or 'wz'. + for zincblende use 'ZB' or 'zb'. + for diamond use 'DM' or 'dm'. + The default is 'WZ'. + eps_n_3d : float, optional (unit: 1e18 cm^-2) + Carrier density below eps_n_3d will be considered as zero. + The default is 1e-14 1e18 cm^-2 == 1e4 cm^-2. + print_log : string, optional => ['high','medium','low', None] + Determines the level of log to be printed. The default is None. + + Returns + ------- + None. + """ + if (psedomorphic_strain == True) and (substrate is None): + raise ValueError('substrate tag can not be None when psedomorphic_strain=True.') + _MobilityCarrier.__init__(self, compositions=compositions, binaries=binaries, + alloy=alloy, system=system, psedomorphic_strain=psedomorphic_strain, + substrate=substrate,alloy_type=alloy_type, + print_log=print_log, eps_n=eps_n_3d) + _Mobility3DCarrier.__init__(self) + + def calculate_3D_mobility(self, n_3d=1, n_dis=1, f_dis=0.5, T=300, + alloy_disordered_effect:bool=False, + dislocation_effect:bool=False, + piezoelectric_effect:bool=False, + acoustic_phonon_effect:bool=False, + polar_optical_phonon_effect:bool=False, + total_mobility:bool=True, + calculate_total_mobility_only:bool=False + ): + """ + This function calculates the sheet mobility from different scattering contributions. + The mobility models are implemented based on the following references. + + Ref-1: Rajan et al., Appl. Phys. Lett. 88, 042103 (2006) => alloy disorder, polar optical phonon + Ref-2: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) => Dislocation + Ref-3: Debdeep Jena's thesis, Chapter-6 APPENDIX, Sec. Three-dimensional carriers => Acoustic phonon + + The considered scattering mechanism are: + Alloy disorder limited (AD) + Threading dislocation mediated (DIS) + Piezoelectric effect (PE) + Acoustic phonon (AP) : Deformation potential mediated + Polar optical phonon (POP) + + Units: + n_3d => in 1e18 cm^-3 + c_lattice => in A + a_lattice => in A + sc_potential => in eV + n_dis => 1e10 cm^-2 + f_dis => unit less + E_pop => eV + + Parameters + ---------- + n_3d : 1D float array, optional (unit: 1e18 cm^-3) + Array containing carrier density data for compositions. Array size + should be same as composition arrary. The default is 1. + n_dis : float, optional (unit: 1e10 cm^-2) + Threading dislocation density. The default is 1. + f_dis : float, optional (unit: unitless) + Fraction of dislocation that contributes in scattering. + The default is 0.5. + T : float, optional (unit: K) + Temperature at which mobility calculations will be done. + The default is 300K. + alloy_disordered_effect : bool, optional + Whether to calculate alloy disordered mediated mobility. Or, whether to include + this contribution in total mobility calculation. The default is False. + dislocation_effect : bool, optional + Whether to calculate interface roughness effect mediated mobility. Or, whether to include + this contribution in total mobility calculation. The default is False. + piezoelectric_effect : bool, optional + Whether to calculate piezoelectric effect mediated mobility. Or, whether to include + this contribution in total mobility calculation. The default is False. + acoustic_phonon_effect : bool, optional + Whether to calculate acoustic phonon effect mediated mobility. Or, whether to include + this contribution in total mobility calculation. + This includes only deformation potential mediated scattering. + The default is False. + polar_optical_phonon_effect : bool, optional + Whether to calculate polar optical phonon effect mediated mobility. Or, whether to include + this contribution in total mobility calculation. The default is False. + total_mobility : bool, optional + Whether to calculate total mobility. The default is True. + calculate_total_mobility_only : + Calculate only the total mobility. If False the return data also contains individual + specified contributions. + + Returns + ------- + pandas dataframe of compositions and mobilities (unit: cm^2 V^-1 S^-1). + Total (or individual contributions) sheet mobility. + + """ + + self.alloy_disordered_effect_=alloy_disordered_effect + self.dislocation_effect_=dislocation_effect + self.piezoelectric_effect_=piezoelectric_effect + self.acoustic_phonon_effect_=acoustic_phonon_effect + self.polar_optical_phonon_effect_=polar_optical_phonon_effect + self.only_total_mobility = calculate_total_mobility_only + self.total_mobility_=total_mobility + return self._calculate_3d_mobility(n_3d=n_3d, n_dis=n_dis, f_dis=f_dis, T=T) + + def Dislocation_3D_Tc_Tq_ratio(self, eps_s, n_3d, m_star): + """ + Calculate the ratio of classical (or momentum) tp quantum scattering times + due to charged dislocation scattering. + + Ref: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) + + Parameters + ---------- + eps_s : float or 1d array of float (unit: epsilon_0) + Static dielectic constants of the material. In the unit of vacumm permitivity. + n_3d : float or 1d array of float (unit: 1E18 cm^-3 ) + 3DEG density. + m_star : float or 1d array of float (unit: m0) + Carrier effective mass. In the unit of m0. + + Returns + ------- + float or 1d array of float (unitless) + The tau_c/Tau_q ratio (classical by quantum scattering time). + + """ + return self._ratio_dis_tc_tq(eps_s, n_3d, m_star) class Plottings(_plot_mobilities): """ diff --git a/mobilitypy/src/__init__.py b/mobilitypy/src/__init__.py index a1cad8ff..cce872f 100644 --- a/mobilitypy/src/__init__.py +++ b/mobilitypy/src/__init__.py @@ -1,6 +1,8 @@ from ._alloy_params import _AlloyParams +from ._mobility_carrier_general import _MobilityCarrier from ._mobilities_2d_carrier import _Mobility2DCarrier +from ._mobilities_3d_carrier import _Mobility3DCarrier from .database import database ## ============================================================================== -__all__ = ['_AlloyParams', '_Mobility2DCarrier', 'database'] \ No newline at end of file +__all__ = ['_AlloyParams', '_MobilityCarrier', '_Mobility2DCarrier', '_Mobility3DCarrier', 'database'] \ No newline at end of file diff --git a/mobilitypy/src/_constants.py b/mobilitypy/src/_constants.py index a0a6938..73e47d3 100644 --- a/mobilitypy/src/_constants.py +++ b/mobilitypy/src/_constants.py @@ -8,14 +8,21 @@ h_bar = sc.constants.hbar # J.s e_mass = sc.constants.m_e # kg k_B = sc.constants.Boltzmann # J K^-1 +sqrt_3_by_2 = 0.8660254037844386 # sqrt(3)/2 ## ============================================================================ -## Constants for different mobility equations +## Constants for different 2DEG mobility equations fact_q_TF = 37.79452249229504 # m0 * e^2 / (2pi * eps_0 * h_bar^2) * 1e-9 => nm^-1 fact_b = 9.931409618986013 # (33pi/4 * fact_q_TF)^(1/3) => nm^-1/3 fact_irf_dis = 6.528368003403906e+18 # (m0*e^4)/(h_bar^3*eps_0^2) => s^-1 fact_alloy = 3.7383724882773685e+15 # 3/16*m0*e^2/1e18 => nm s^-1 -m0_by_e = e_mass/e_charge fact_phonon = 8.762231231847618e+10 # m0*e^2*k_b/(pi*h_bar^3) => kg.K^-1J^2s^-3 fact_pop_k0 = 5.123167219674931 # sqrt(2*m_0*e/h_bar^2)*1e9 => nm^-1 fact_pop_y = 2.777985128879875e+3 # #(pi*h_bar^2)*1e18/m_0/k_B -fact_pop = 1.8039021162385878e+17 # 1e-9*e^2*e*m_0/(2*eps_0*h_bar^3) \ No newline at end of file +fact_pop = 1.8039021162385878e+17 # 1e-9*e^2*e*m_0/(2*eps_0*h_bar^3) +## ============================================================================ +## Constants for different 3DEG mobility equations +#omega_0 * n_3d = X * 1e-24 cm^3 * Y * 1e18 cm^-3 = XY * 1e-6 +fact_ad_3d = 21.1699056589355 # (2*e*h_bar*k_B)/(3*pi*m0*e^2) * 1e10 => cm^-2.V-1.s-1 +fact_Nc_3d = 4.829366079143929 # 2*((m0*k_B)**(3/2)) / (2*pi*h_bar**2)**(3/2) * 1e-6 => 1e15 cm^-3 +fact_pop_3d = 0.156926697141818 # 4.0*pi*eps_0*h_bar*h_bar/(sqrt(2)*(e*m0)**(3/2))*1e4 => cm^2V^-1s^-1 +fact_dp_3d = 1.5333300251501703e-6 # 2*h_bar*e*1e-2/(3*pi*m0*e*e*1e18) => cm^2V^-1s^-1 diff --git a/mobilitypy/src/_mobilities_2d_carrier.py b/mobilitypy/src/_mobilities_2d_carrier.py index fafa047..fca5154 100644 --- a/mobilitypy/src/_mobilities_2d_carrier.py +++ b/mobilitypy/src/_mobilities_2d_carrier.py @@ -1,11 +1,10 @@ import numpy as np import pandas as pd import scipy as sc -from ._alloy_params import _AlloyParams from ._constants import * ## ============================================================================== -class _Mobility2DCarrier(_AlloyParams): +class _Mobility2DCarrier: ''' The functions in this class calculates the mobility of 2D carrier gas. The mobility models are implemented based on the following references. @@ -20,142 +19,21 @@ class _Mobility2DCarrier(_AlloyParams): heterostructures with varied Al content. Sci. China Ser. F-Inf. Sci. 51, 780–789 (2008). https://doi.org/10.1007/s11432-008-0056-7 - Ref-3: Mondal et. al., TBA + Ref-3: Mondal et. al., Interplay of carrier density and mobility in Al-rich (Al,Ga)N-channel HEMTs: + Impact on high-power device performance potential. APL Electronic Devices 1, 026117 (2025) + https://doi.org/10.1063/5.0277051 ''' - def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', - system='ternary', psedomorphic_strain=False, substrate=None, - alloy_type='WZ', print_log=None, eps_n_2d=1e-10): + def __init__(self): """ Initiation function of the class _Mobility2DCarrier. - Parameters - ---------- - compositions : 1D array of float, optional - The alloy mole fractions. E.g. x values in Si_xGe_1-x. The default is None. - If None, a composition array is generated using `np.linspace(start=0.01, end=0.99, num=101)`. - binaries : list of strings (case sensitive), optional - Name of the corresponding binaries of requested alloy. They should - match the names in database. All implemented materials name list - can be found in the README. For ternary alloy 'compositions' correspond - to the 1st binary in the list; for quaternaries 1st binary is 1st composition - and so on (from left to right). The default is ['AlN', 'GaN']. - alloy : string (case sensitive), optional - The alloy name. The name should match the name in database. All - implemented materials name list can be found in the README. Case sensitive. - The default is 'AlGaN'. - system : string (case sensitive), optional - Type of the alloy. E.g. 'ternary'. - The default is 'ternary'. - psedomorphic_strain : bool, optional - Whether to consider pseudomorphic strain. - The default is False. - substrate : string or float (in Angstrom), optional - The substrate name (if string, warning: the name should be in the database) - or the substrate in-plane lattice parameter (if float, Angstrom unit). - The default is None. Error will be raised if substrate=None and - psedomorphic_strain=True. - alloy_type : str, optional (case insensitive) - The crystal type of alloy. This will be considered when calculating - parameters like Poisson ratio etc. - Use following abbreviation name: - for wurtzite use 'WZ' or 'wz'. - for zincblende use 'ZB' or 'zb'. - for diamond use 'DM' or 'dm'. - The default is 'WZ'. - print_log : string, optional => ['high','medium','low', None] - Determines the level of log to be printed. The default is None. - eps_n_2d : float, optional - Carrier density below eps_n_2d will be considered as zero. - The default is 1e-10. - Returns ------- None. """ - self.print_info = print_log - if self.print_info is not None: self.print_info = self.print_info.lower() - - self.eps_n_2d = eps_n_2d - - _AlloyParams.__init__(self, compositions=compositions, binaries=binaries, alloy=alloy) - self._get_alloy_params(system=system) - - if psedomorphic_strain: - self.alloy_type_ = alloy_type - if isinstance(substrate, str): - substrate_params_dic = _AlloyParams._get_substrate_properties(substrate) - substrate_lp = substrate_params_dic.get('lattice_a0') - else: - substrate_lp = float(substrate) - - lattice_a = self.alloy_params_.get('lattice_a0') - lattice_c = self.alloy_params_.get('lattice_c0') - epsilon_zz = self._get_Poisson_ratio()*((substrate_lp - lattice_a) / lattice_a) - # Re-populate the lattice parameters - self.alloy_params_['lattice_a0'] = np.array([substrate_lp]*len(lattice_a)) - self.alloy_params_['lattice_c0']= lattice_c * (1.0 + epsilon_zz) - - @staticmethod - def _calculate_sheet_resitance(n_2d, mobility): - """ - This function calculates the sheet resistance. - - Units: - n_2d => in nm^-2 - e => 1.602176634e-19 C - mobility (mu) => cm^2 V^-1 s^-1 - - 1 coulomb/volt = 1 second/ohm - 1 ohm = 1 C^-1.V.s - - R = 1/(e * n_2d * mu) ohm/square - = 1/(1.602176634e-19*1e14 *n_2d * mu C.cm^-2.cm^2.V^-1.S^-1) - = 62415.09074/(n_2d * mu) ohm/square - - Parameters - ---------- - n_2d : 1D float array (nm^-2) - Array containing carrier density data for compositions. This can be - a single number as well. Then all compositions will have same carrier - density. - mobility : 1D float array (cm^2 V^-1 s^-1) - Array containing mobility data for compositions. - - Returns - ------- - 1D float array (ohm/square) - Sheet resistance for compositions. - - """ - return 62415.09074/(n_2d * mobility) - - @staticmethod - def _apply_Varshni_T_correction_2_bandgap(bandgap_0, temp:float=300, - bandgap_alpha:float=0, bandgap_beta:float=0): - """ - This functions applies Varshni's formula for temperature correction to band gap. - Eg(T) = Eg(T=0) - [aT^2/(T+b)] - - Parameters - ---------- - bandgap_0 : 1D float array (eV) - Band gap values at 0K temperature. - temp : float, optional (K) - Temperature in K. The default is 300K. - bandgap_alpha : float, optional (eV/K) - Temperature correction coefficient alpha. The default is 0. - bandgap_beta : float, optional (K) - Temperature correction coefficient beta. The default is 0. - - Returns - ------- - 1D float array (eV) - The temperature corrected band gap values. - - """ - return bandgap_0 - (bandgap_alpha*temp*temp/(temp+bandgap_beta)) + self.eps_n_2d = self.eps_n def _calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, mode:str='LFOM', T_corect_bandgap:bool=False, @@ -188,13 +66,13 @@ def _calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, mode:str='L Parameters ---------- - n_2d : 1D float array (nm^-2) + n_2d : 1D float array (unit: nm^-2) Array containing carrier density data for compositions. This can be a single number as well. Then all compositions will have same carrier density. - mobility : 1D float array (cm^2 V^-1 s^-1) + mobility : 1D float array (unit: cm^2 V^-1 s^-1) Array containing mobility data for compositions. - temp : float, optional (K) + temp : float, optional (unit: K) Temperature for band gap correction. The default is 300K. mode : str, optional (['LFOM']) The figure-of-merit name. The default is 'LFOM'. @@ -207,7 +85,7 @@ def _calculate_figure_of_merit(self, n_2d, mobility, temp:float=300, mode:str='L Returns ------- - 1D float array (MW/cm^2) + 1D float array (unit: MW/cm^2) Figure-of-merit. """ @@ -244,7 +122,9 @@ def _calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, heterostructures with varied Al content. Sci. China Ser. F-Inf. Sci. 51, 780–789 (2008). https://doi.org/10.1007/s11432-008-0056-7 - Ref-3: Mondal et. al., TBA + Ref-3: Mondal et. al., Interplay of carrier density and mobility in Al-rich (Al,Ga)N-channel HEMTs: + Impact on high-power device performance potential. APL Electronic Devices 1, 026117 (2025) + https://doi.org/10.1063/5.0277051 The considered scattering mechanism are: Interface roughness mediated (IRF) @@ -268,21 +148,21 @@ def _calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, Parameters ---------- - n_2d : 1D float array or float (nm^-2) + n_2d : 1D float array or float, optional (unit: nm^-2) Array containing carrier density data for compositions. This can be a single number as well. Then all compositions will have same carrier - density. - rms_roughness : float, optional (nm) + density. The default is 0.1 + rms_roughness : float, optional (unit: nm) Interface root-mean-squared roughness for interface-roughness scattering contribution. The default is 0.1. - corr_len : float, optional (nm) + corr_len : float, optional (unit: nm) Correlation length of interface roughness. The default is 1. - n_dis : float, optional (nm^-2) + n_dis : float, optional (unit: nm^-2) Threading dislocation density. The default is 1. - f_dis : float, optional + f_dis : float, optional (unit: unitless) Fraction of dislocation that contributes in scattering. The default is 0.1. - T : float, optional (K) + T : float, optional (unit: K) Temperature at which mobility calculations will be done. The default is 300K. return_sc_rates : float, optional @@ -290,7 +170,7 @@ def _calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, Returns ------- - pandas dataframe of compositions and mobilities + pandas dataframe of compositions and mobilities (unit: cm^2 V^-1 S^-1) Total (or individual contributions) sheet mobility. If return_sc_rates=True, then scattering rates are also returned. @@ -318,7 +198,8 @@ def _calculate_sheet_mobility(self, n_2d=0.1, rms_roughness=0.1, corr_len=1, high_frequency_dielectric_constant[ii], lattice_c[ii], lattice_a[ii], sc_potential[ii], self.comps_[ii], n_2d[ii], rms_roughness, corr_len, n_dis, f_dis, - T, electromech_coupling_sqr[ii], deformation_pot[ii], mass_densitty[ii], LA_velocity[ii], POP_energy[ii]) + T, electromech_coupling_sqr[ii], deformation_pot[ii], + mass_densitty[ii], LA_velocity[ii], POP_energy[ii]) self._print_database_params() # mobility unit: cm^2 V^-1 S^-1 if self.print_info is not None: print(f'- Composition: {self.comps_[ii]:.5f}') @@ -409,24 +290,11 @@ def _set_params(self, m_star, eps_s, eps_h, c_lattice, a_lattice, sc_potential, """ This function sets the parameters for mobility calculations. """ - self.m_star_ = m_star - self.eps_s_ = eps_s - self.eps_h_ = eps_h - self.c_lp = c_lattice - self.a_lp = a_lattice - self.sc_potential_ = sc_potential + self._set_params_general(m_star, eps_s, eps_h, c_lattice, a_lattice, sc_potential, + n_dis, f_dis, mass_density, v_LA, E_pop, T, K_square, + E_D, rms_roughness, corr_len) self.comp_ = alloy_composition self.n_2d_ = n_2d - self.corr_len_ = corr_len - self.rms_roughness_ = rms_roughness - self.n_dislocation_ = n_dis - self.f_dislocation_ = f_dis - self.temp_ = T - self.K_sqr = K_square - self.E_d = E_D - self.mass_density_ = mass_density - self.v_LA = v_LA - self.E_pop = E_pop self._get_derived_params() def _get_derived_params(self): @@ -436,8 +304,6 @@ def _get_derived_params(self): self.q_TF = fact_q_TF * tmp_ # nm^-1 self.b_ = fact_b*(self.n_2d_*tmp_)**(1/3) # nm^-1 self.fact_1 = fact_irf_dis * tmp_ /self.eps_s_ # s^-1 - self.omega = 0.8660254037844386 * self.a_lp**2 * self.c_lp # sqrt(3)/2 * a^2 c => nm^3 - self.m0_by_e_ = self.m_star_ * m0_by_e self.k_0 = fact_pop_k0*np.sqrt(self.m_star_*self.E_pop) # nm^-1 def _print_database_params(self): @@ -451,12 +317,7 @@ def _print_database_params(self): """ if self.print_info == 'high': print(f'- Composition={self.comp_:.5f}') - print(f'\t-- a={self.a_lp:.5f} nm | c={self.c_lp:.5f} nm | m*={self.m_star_:.5f} m0 | eps_s={self.eps_s_:.5f} eps0 | eps_h={self.eps_h_:.5f} eps0') - print(f'\t-- Mass density={self.mass_density_:.2f} | scattering potential={self.sc_potential_:.2f} eV | 2deg density={self.n_2d_:.5f} nm^-2 | T={self.temp_:.1f} K') - print(f'\t-- Interface rms roughness={self.rms_roughness_:.3f} nm | correlation length={self.corr_len_:.3f} nm') - print(f'\t-- Dislocation density={self.n_dislocation_:.4f} nm^-2 | dislocation occupancy={self.f_dislocation_:.1f}') - print(f'\t-- Electromechanical coupling coefficient={self.K_sqr:.5f} | deformation potential={self.E_d:.5f}') - print(f'\t-- Longitudinal acoustic phonon velocity={self.v_LA:.2f} m/s | polar optical phonon energy={self.E_pop:.5f} eV') + self._print_database_params_general() print(f'\t-- Fermi wave vector={self.k_F} | b={self.b_}') print('') diff --git a/mobilitypy/src/_mobilities_3d_carrier.py b/mobilitypy/src/_mobilities_3d_carrier.py new file mode 100644 index 0000000..1500607 --- /dev/null +++ b/mobilitypy/src/_mobilities_3d_carrier.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Dec 4 16:15:33 2025 + +@author: badal.mondal +""" +import numpy as np +import pandas as pd +from ._constants import fact_ad_3d, fact_Nc_3d, fact_pop_3d, fact_dp_3d + +## ============================================================================ +class _Mobility3DCarrier: + ''' + The functions in this class calculates the mobility of 3D carrier gas. + The mobility models are implemented based on the following references. + + Note: Some of the equations in the references has prining mistakes. The mistakes + are corrected in our implementation. + + Ref-1: Rajan et al., Appl. Phys. Lett. 88, 042103 (2006) => alloy disorder, polar optical phonon + Ref-2: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) => Dislocation + Ref-3: Debdeep Jena's thesis, Chapter-6 APPENDIX, Sec. Three-dimensional carriers => Acoustic phonon + + ''' + + def __init__(self): + """ + Initiation function of the class _Mobility3DCarrier. + + Returns + ------- + None. + + """ + self.eps_n_3d = self.eps_n + + def _calculate_3d_mobility(self, n_3d=1, n_dis=1, f_dis=0.5, T=300): + """ + This function calculates the sheet mobility from different scattering contributions. + The mobility models are implemented based on the following references. + + Ref-1: Rajan et al., Appl. Phys. Lett. 88, 042103 (2006) => alloy disorder, polar optical phonon + Ref-2: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) => Dislocation + Ref-3: Debdeep Jena's thesis, Chapter-6 APPENDIX, Sec. Three-dimensional carriers => Acoustic phonon + + The considered scattering mechanism are: + Alloy disorder limited (AD) + Threading dislocation mediated (DIS) + Piezoelectric effect (PE) + Acoustic phonon (AP) : Deformation potential mediated + Polar optical phonon (POP) + + Units: + n_3d => in 1e18 cm^-3 + c_lattice => in A + a_lattice => in A + sc_potential => in eV + n_dis => 1e10 cm^-2 + f_dis => unit less + E_pop => eV + + Parameters + ---------- + n_3d : 1D float array, optional (unit: 1e18 cm^-3) + Array containing carrier density data for compositions. Array size + should be same as composition arrary. The default is 1. + n_dis : float, optional (unit: 1e10 cm^-2) + Threading dislocation density. The default is 1. + f_dis : float, optional (unit: unitless) + Fraction of dislocation that contributes in scattering. + The default is 0.5. + T : float, optional (unit: K) + Temperature at which mobility calculations will be done. + The default is 300K. + + Returns + ------- + pandas dataframe of compositions and mobilities (unit: cm^2 V^-1 S^-1). + Total (or individual contributions) sheet mobility. + + """ + self._set_params_general(self.alloy_params_.get('e_effective_mass'), + self.alloy_params_.get('static_dielectric_constant'), + self.alloy_params_.get('high_frequency_dielectric_constant'), + self.alloy_params_.get('lattice_c0'), + self.alloy_params_.get('lattice_a0'), + self.alloy_params_.get('alloy_scattering_potential'), + n_dis, f_dis, + self.alloy_params_.get('mass_density'), + self.alloy_params_.get('LA_phonon_velocity'), + self.alloy_params_.get('PO_phonon_energy') + ) + # Remove small values for the n_3d to avoid 0-division + self.n_3d_ = np.where(n_3d < self.eps_n_3d, np.nan, n_3d) + self._print_database_params() + + mobility = {} + total_inv_mu = 0 + if self.only_total_mobility: + if self.print_info is not None: print('\t-- Calculating only total mobility') + if self.alloy_disordered_effect_: total_inv_mu += 1/self._alloy_disorder_mu() + if self.polar_optical_phonon_effect_: total_inv_mu += 1/self._pop_mu() + if self.acoustic_phonon_effect_: total_inv_mu += 1/self._ac_dp_mu() # due to deformation_potential_effect + #if self.piezoelectric_effect_: total_inv_mu += self._inv_tau_pe() + if self.dislocation_effect_: total_inv_mu += 1/self._dis_mu() + mobility['TOT'] = 1/total_inv_mu + else: + if self.alloy_disordered_effect_: + if self.print_info is not None: print('\t-- Calculating alloy-disordered mobility') + _mu_contrib = self._alloy_disorder_mu() + total_inv_mu += 1/_mu_contrib + mobility['AD'] = _mu_contrib + + if self.polar_optical_phonon_effect_: + if self.print_info is not None: print('\t-- Calculating polar optical phonon effect mobility') + _mu_contrib = self._pop_mu() + total_inv_mu += 1/_mu_contrib + mobility['POP'] = _mu_contrib + + if self.acoustic_phonon_effect_: + if self.print_info is not None: print('\t-- Calculating acoustic phonon effect mobility') + _mu_contrib = self._ac_dp_mu() + total_inv_mu += 1/_mu_contrib + mobility['AP'] = _mu_contrib + + # if self.piezoelectric_effect_: + # if self.print_info is not None: print('\t--- Calculating piezoelectric effect mobility') + # _mu_contrib = self._inv_sc_pe() + # total_inv_mu += 1/_mu_contrib + # mobility['PE'] = _mu_contrib + + if self.dislocation_effect_: + if self.print_info is not None: print('\t-- Calculating dislocation effect mobility') + _mu_contrib = self._dis_mu() + total_inv_mu += 1/_mu_contrib + mobility['DIS'] = _mu_contrib + + if self.total_mobility_: + if self.print_info is not None: print('\t-- Calculating total mobility') + mobility['TOT'] = 1/total_inv_mu + + if self.print_info is not None: print(f'{"="*72}') + return pd.DataFrame.from_dict(mobility, orient='index') + + def _ln_1p_exp_xi(self): + Nc_3d = fact_Nc_3d * (self.m_star_*self.temp_)**(3/2) #1e15 cm^-3 + n_by_Nc = self.n_3d_ / Nc_3d * 1e3 # 1E18 cm^-3 / 1e15 cm^-3 = 1e3 + #---------------------------------------------------------------------- + # Joyce-Dixon approximation (APL 31, 354 (1977)) + # Upto cube term is considered + exp_xi = np.exp(np.log(n_by_Nc) + 3.535534e-1*n_by_Nc - 4.95009e-3*n_by_Nc*n_by_Nc + + 1.48386e-4*n_by_Nc*n_by_Nc*n_by_Nc) + return np.log(1+exp_xi) + + ## Alloy disordered limited mobility + def _alloy_disorder_mu(self, eps_den = 1e-8): + + #---------------------------------------------------------------------- + demoninator_ = self.m_star_*self.sc_potential_*self.sc_potential_*self.omega \ + *self.n_3d*self.comp_*(1.0-self.comp_) + # Remove small values for both the n_3d and comp_ or (1-comp_) + demoninator_ = np.where(demoninator_ < eps_den, np.nan, demoninator_) + return fact_ad_3d * self.temp_ * self._ln_1p_exp_xi() / demoninator_ # cm^2.V^-1.s^-1 + + ## Polar optical phonon limited mobility + def _pop_mu(self): + eps_star = 1/(1/self.eps_h_ - 1/self.eps_s_) + exp_fact = np.exp(self.E_pop/self.temp_*11604.518121550082) - 1.0 + return fact_pop_3d * eps_star * exp_fact / self.m_star_ / np.sqrt(self.m_star_*self.E_pop) + + ## Deformation potential acoustic phonon limited mobility + def _ac_dp_mu(self): + numerator = fact_dp_3d * self.mass_density_ * self.v_LA * self.v_LA * self._ln_1p_exp_xi() + return numerator / (self.n_3d*self.m_star_*self.E_d*self.E_d) + + ## Dislocation limited mobility + def _dis_mu(self): + #(3*pi_*pi_)**(1/3) = 3.0936677262801355e6 cm^-1 + fermi_wave_vect = 3.0936677262801355*(self.n_3d**(1/3)) # 1e6 cm^-1 + fermi_wave_vect_sq = fermi_wave_vect*fermi_wave_vect # 1e12 cm^-2 + # h_bar*h_bar/2/e_mass*1e12 = 6.10426432246119e-27 J^2s^2cm^-2kg^-1 + fermi_energy = 6.10426432246119*fermi_wave_vect_sq/self.m_star_ # 1e-27 J^2s^2cm^-2kg^-1 + # 2*eps_0/3/e_charge/e_charge*1e-43 = 2.2995173111302578e-17 cm^2 + TF_screening_len_sq = 2.2995173111302578*self.eps_s_*fermi_energy/self.n_3d # 1e-17 cm^2 + # k_f^2*lamda_TF^2 => 1e-5 + fact_12 = (1+4*fermi_wave_vect_sq*TF_screening_len_sq*1e-5)**(3/2)/TF_screening_len_sq**2 + # h_bar*h_bar*h_bar*eps_0*eps_0/e_charge**3/e_mass**2*1e22/1e10 = 26941.18974076079 + fact_11 = (self.eps_s_*self.c_lp/self.f_dislocation_/self.m_star_)**2 / self.n_dislocation_ + return fact_11 * fact_12 * 26941.18974076079 + + def _print_database_params(self): + """ + This function prints the log of model descriptions. + + Returns + ------- + None. + + """ + if self.print_info == 'high': + self._print_database_params_general() + print('') diff --git a/mobilitypy/src/_mobility_carrier_general.py b/mobilitypy/src/_mobility_carrier_general.py new file mode 100644 index 0000000..6a28b50 --- /dev/null +++ b/mobilitypy/src/_mobility_carrier_general.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Dec 4 15:41:33 2025 + +@author: badal.mondal +""" + +import numpy as np +from ._alloy_params import _AlloyParams +from ._constants import sqrt_3_by_2, e_mass, e_charge + +## ============================================================================ +class _MobilityCarrier(_AlloyParams): + ''' + The functions in this class sets general parameters for the mobility of nD carrier gas. + ''' + + def __init__(self, compositions=None, binaries=['AlN', 'GaN'], alloy='AlGaN', + system='ternary', psedomorphic_strain=False, substrate=None, + alloy_type='WZ', print_log=None, eps_n=1e-10): + """ + Initiation function of the class _MobilityCarrier. + + Parameters + ---------- + compositions : 1D array of float, optional + The alloy mole fractions. E.g. x values in Si_xGe_1-x. The default is None. + If None, a composition array is generated using `np.linspace(start=0.01, end=0.99, num=101)`. + binaries : list of strings (case sensitive), optional + Name of the corresponding binaries of requested alloy. They should + match the names in database. All implemented materials name list + can be found in the README. For ternary alloy 'compositions' correspond + to the 1st binary in the list; for quaternaries 1st binary is 1st composition + and so on (from left to right). The default is ['AlN', 'GaN']. + alloy : string (case sensitive), optional + The alloy name. The name should match the name in database. All + implemented materials name list can be found in the README. Case sensitive. + The default is 'AlGaN'. + system : string (case sensitive), optional + Type of the alloy. E.g. 'ternary'. + The default is 'ternary'. + psedomorphic_strain : bool, optional + Whether to consider pseudomorphic strain. + The default is False. + substrate : string or float (unit: Angstrom), optional + The substrate name (if string, warning: the name should be in the database) + or the substrate in-plane lattice parameter (if float, Angstrom unit). + The default is None. Error will be raised if substrate=None and + psedomorphic_strain=True. + alloy_type : str, optional (case insensitive) + The crystal type of alloy. This will be considered when calculating + parameters like Poisson ratio etc. + Use following abbreviation name: + for wurtzite use 'WZ' or 'wz'. + for zincblende use 'ZB' or 'zb'. + for diamond use 'DM' or 'dm'. + The default is 'WZ'. + print_log : string, optional => ['high','medium','low', None] + Determines the level of log to be printed. The default is None. + eps_n : float, optional (unit: nm^-2 for 2DEG or 1e18 cm^-2 for 3DG) + Carrier density below eps_n will be considered as zero. + For 2DEG: The default is 1e-10 nm^-2 == 1e4 cm^-2. + For 3DEG: The default is 1e-14 1e18 cm^-2 == 1e4 cm^-2. + + Returns + ------- + None. + + """ + self.print_info = print_log + if self.print_info is not None: self.print_info = self.print_info.lower() + + self.eps_n = eps_n + + _AlloyParams.__init__(self, compositions=compositions, binaries=binaries, alloy=alloy) + self._get_alloy_params(system=system) + + if psedomorphic_strain: + self.alloy_type_ = alloy_type + if isinstance(substrate, str): + substrate_params_dic = _AlloyParams._get_substrate_properties(substrate) + substrate_lp = substrate_params_dic.get('lattice_a0') + else: + substrate_lp = float(substrate) + + lattice_a = self.alloy_params_.get('lattice_a0') + lattice_c = self.alloy_params_.get('lattice_c0') + epsilon_zz = self._get_Poisson_ratio()*((substrate_lp - lattice_a) / lattice_a) + # Re-populate the lattice parameters + self.alloy_params_['lattice_a0'] = np.array([substrate_lp]*len(lattice_a)) + self.alloy_params_['lattice_c0']= lattice_c * (1.0 + epsilon_zz) + + def _set_params_general(self, m_star, eps_s, eps_h, c_lattice, a_lattice, sc_potential, + n_dis, f_dis, mass_density, v_LA, E_pop, T, + K_square=None, E_D=None, rms_roughness=None, corr_len=None): + """ + This function sets the parameters for mobility calculations. + """ + self.m_star_ = m_star + self.eps_s_ = eps_s + self.eps_h_ = eps_h + self.c_lp = c_lattice + self.a_lp = a_lattice + self.sc_potential_ = sc_potential + self.n_dislocation_ = n_dis + self.f_dislocation_ = f_dis + self.mass_density_ = mass_density + self.v_LA = v_LA + self.E_pop = E_pop + self.temp_ = T if T > 1e-8 else 1e-5 # Make sure zero divison does not happen when T=0 is choosen + self.omega = sqrt_3_by_2 * self.a_lp**2 * self.c_lp # sqrt(3)/2 * a^2 c + self.m0_by_e_ = self.m_star_ * e_mass / e_charge + #========================================== + self.K_sqr = K_square + self.E_d = E_D + self.corr_len_ = corr_len + self.rms_roughness_ = rms_roughness + + def _print_database_params_general(self): + """ + This function prints the log of model descriptions. + + Returns + ------- + None. + + """ + if self.print_info == 'high': + print(f'\t-- a={self.a_lp:.5f} nm | c={self.c_lp:.5f} nm | m*={self.m_star_:.5f} m0 | eps_s={self.eps_s_:.5f} eps0 | eps_h={self.eps_h_:.5f} eps0') + print(f'\t-- Mass density={self.mass_density_:.2f} | scattering potential={self.sc_potential_:.2f} eV | T={self.temp_:.1f} K') + if (self.rms_roughness_ is not None) and (self.corr_len_ is not None): + print(f'\t-- Interface rms roughness={self.rms_roughness_:.3f} nm | correlation length={self.corr_len_:.3f} nm') + print(f'\t-- Dislocation density={self.n_dislocation_:.4f} nm^-2 | dislocation occupancy={self.f_dislocation_:.1f}') + if (self.K_sqr is not None) and (self.E_d is not None): + print(f'\t-- Electromechanical coupling coefficient={self.K_sqr:.5f} | deformation potential={self.E_d:.5f}') + print(f'\t-- Longitudinal acoustic phonon velocity={self.v_LA:.2f} m/s | polar optical phonon energy={self.E_pop:.5f} eV') + print('') + + @staticmethod + def _calculate_sheet_resitance(carrier_density, mobility): + """ + This function calculates the sheet resistance. + + Units: + carrier_density => in nm^-2 + e => 1.602176634e-19 C + mobility (mu) => cm^2 V^-1 s^-1 + + 1 coulomb/volt = 1 second/ohm + 1 ohm = 1 C^-1.V.s + + R = 1/(e * carrier_density * mu) ohm/square + = 1/(1.602176634e-19*1e14 *carrier_density * mu C.cm^-2.cm^2.V^-1.S^-1) + = 62415.09074/(carrier_density * mu) ohm/square + + Parameters + ---------- + carrier_density : 1D float array (unit: nm^-2) + Array containing carrier density data for compositions. This can be + a single number as well. Then all compositions will have same carrier + density. + mobility : 1D float array (unit: cm^2 V^-1 s^-1) + Array containing mobility data for compositions. + + Returns + ------- + 1D float array (unit: ohm/square) + Sheet resistance for compositions. + + """ + return 62415.09074/(carrier_density * mobility) + + @staticmethod + def _apply_Varshni_T_correction_2_bandgap(bandgap_0, temp:float=300, + bandgap_alpha:float=0, bandgap_beta:float=0): + """ + This functions applies Varshni's formula for temperature correction to band gap. + Eg(T) = Eg(T=0) - [aT^2/(T+b)] + + Parameters + ---------- + bandgap_0 : 1D float array (unit: eV) + Band gap values at 0K temperature. + temp : float, optional (unit: K) + Temperature in K. The default is 300K. + bandgap_alpha : float, optional (unit: eV/K) + Temperature correction coefficient alpha. The default is 0. + bandgap_beta : float, optional (unit: K) + Temperature correction coefficient beta. The default is 0. + + Returns + ------- + 1D float array (unit: eV) + The temperature corrected band gap values. + + """ + return bandgap_0 - (bandgap_alpha*temp*temp/(temp+bandgap_beta)) + + @staticmethod + def _ratio_dis_tc_tq(eps_s, n_3d, m_star): + """ + Calculate the ratio of classical (or momentum) tp quantum scattering times + due to charged dislocation scattering. + + Ref: DJ. and UKM., PRB 66, 241307(R) (2002) and DJ. et al., PRB 67, 153306 (2003) + + Parameters + ---------- + eps_s : float or 1d array of float (unit: epsilon_0) + Static dielectic constants of the material. In the unit of vacumm permitivity. + n_3d : float or 1d array of float (unit: 1E18 cm^-3 ) + 3DEG density. + m_star : float or 1d array of float (unit: m0) + Carrier effective mass. In the unit of m0. + + Returns + ------- + float or 1d array of float (unitless) + The tau_c/Tau_q ratio (classical by quantum scattering time). + + """ + #2*eps_0*h_bar**2*pi_**(8/3)*3**(1/3)/e_charge/e_charge/e_mass*1e8 = 0.025715482457837318 + return 1 + 0.025715482457837318*eps_s*n_3d**(1/3)/m_star \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..28ec465 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,47 @@ +[build-system] +requires = ["setuptools", "setuptools-scm"] +build-backend = "setuptools.build_meta" + +[project] +name = "mobilitypy" +version = "1.0.4" +#dynamic = ["version"] +dependencies = [ + 'numpy', + 'scipy', + 'matplotlib' , + 'pandas' +] +requires-python = ">=3.12" +authors = [ + { name = "Badal Mondal", email = "badalmondal.chembgc@gmail.com" }, +] +maintainers = [ + {name = name = "Badal Mondal", email = "badalmondal.chembgc@gmail.com" }, +] +description = "mobilitypy: Python package for mobility calculations in semiconductors" +readme = "README.md" +license = "GPL-3.0-only" +license-files = ["LICEN[CS]E*"] +keywords = ["mobility", "Electron gas", "2DEG", "3DEG", "Alloy disorder"] +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Programming Language :: Python :: 3", + "Operating System :: OS Independent", +] + +[project.urls] +Homepage = "https://github.com/SemiconductorTransport/mobilitypy" +Documentation = "https://github.com/SemiconductorTransport/mobilitypy/wiki" +Repository = "https://github.com/SemiconductorTransport/mobilitypy.git" +Issues = "https://github.com/SemiconductorTransport/mobilitypy/issues" +Changelog = "https://github.com/SemiconductorTransport/mobilitypy/wiki/Brief-release-history" +Releasenotes = "https://github.com/SemiconductorTransport/mobilitypy/releases" + +[tool.pytest.ini_options] +minversion = "7.0" +addopts = "-q" +testpaths = ["tests"] + +[project.optional-dependencies] +test = ["pytest>=7.0", "pytest-cov>=4.1"] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 12ce517..0000000 --- a/setup.cfg +++ /dev/null @@ -1,3 +0,0 @@ -[metadata] -version = attr: mobilitypy.__version__ - diff --git a/setup.py b/setup.py deleted file mode 100644 index ddec54d..0000000 --- a/setup.py +++ /dev/null @@ -1,25 +0,0 @@ -## python3 setup.py bdist_wheel -## python3 -m twine upload dist/* -import setuptools - -with open("README.md", "r") as fh: - long_description = fh.read() - -setuptools.setup( - name='mobilitypy', - author="Badal Mondal", - author_email="badalmondal.chembgc@gmail.com", - maintainer="Badal Mondal", - maintainer_email="badalmondal.chembgc@gmail.com", - description="mobilitypy: Python package for mobility calculations in semiconductors", - long_description=long_description, - long_description_content_type="text/markdown", - install_requires=['numpy', 'scipy', 'matplotlib' ,'pandas'], - url="https://github.com/SemiconductorTransport/mobilitypy", - packages=setuptools.find_packages(), - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", - "Operating System :: OS Independent", - ], - ) diff --git a/tutorials/01_2deg_mobility_calculation.ipynb b/tutorials/01_2deg_mobility_calculation.ipynb index 8623f31..3fc1f8a 100644 --- a/tutorials/01_2deg_mobility_calculation.ipynb +++ b/tutorials/01_2deg_mobility_calculation.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f8068cb9-b57a-4141-a049-2bd20c4285cf", "metadata": {}, "outputs": [], @@ -42,12 +42,13 @@ "import numpy as np\n", "import scipy as sc\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "#import mobilitypy as mpy_old" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "89f5eeaa-a049-481e-be9b-460d297a585f", "metadata": {}, "outputs": [], @@ -58,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "ea470300-8e85-45a2-a94a-c4489518b2de", "metadata": {}, "outputs": [], @@ -72,14 +73,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "c71e0228-1fe1-4078-b544-eef29da56334", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/local/MyGitHub/mobilitypy/tutorials/../mobilitypy']\n" + ] + } + ], + "source": [ + "import mobilitypy\n", + "print(mobilitypy.__path__)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "2bddf97e-0bde-4b7e-aeea-827e2bd52b7d", "metadata": {}, "outputs": [], "source": [ "save_figure_dir = os.path.join(mobilitypy_tutorial_path,'../imgs')\n", "save_file_name = 'mobilities'\n", - "savefigure = True\n", + "savefigure = False\n", "fig_dpi = 75" ] }, @@ -93,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6fc041c7-180f-4a4d-8b36-72108cbc65eb", "metadata": { "scrolled": true @@ -128,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "be88c1aa-5795-4d7a-8959-271bc69e16f9", "metadata": {}, "outputs": [], @@ -139,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "ef4a1498-1052-4369-96f7-a71b7a374fe9", "metadata": { "scrolled": true @@ -162,17 +182,222 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "c4139416-6a71-457b-b765-0923ba252a04", + "execution_count": 10, + "id": "e59882f2-793f-4475-b9a2-eb80ee31dfa8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "save_file_name_ = f'{save_file_name}_{T}K.png'\n", + "print(f'Save file name: {save_file_name_}')\n", "fig, ax,_ = plt2deg.plot_2d_carrier_mobilities(mobility_df, save_file_name=save_file_name_,\n", " ymin=None, ymax=None, xmax=1, xmin=0, y_scale_log=True, \n", " annotate_pos=(11,11), show_right_ticks=True,\n", @@ -243,10 +487,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4ae4bde8-f37a-4d8d-87e6-db1234699d8e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "save_file_name_ = f'LFOM_{T}K.png'\n", "fig, ax, _ = plt2deg.plot_2d(data_FOM_2_plot, save_file_name=save_file_name_,\n", @@ -260,10 +525,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "99456cc0-d226-43d9-ad6c-9c2a3bcb7e29", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "save_file_name_ = f'LFOM_norm_{T}K.png'\n", "YY = data_FOM_2_plot.copy()\n", @@ -279,10 +555,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "85ed27df-408e-48d0-bcdb-e46b1185e0fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "save_file_name_ = f'sheet_resistamce_{T}K.png'\n", "#data_sheet_resitance_2_plot[:, 1] /= data_sheet_resitance_2_plot[0, 1]\n", @@ -294,6 +581,14 @@ "#ax.axhline(y=data_FOM_2_plot[0,1], c='k', ls='--')\n", "plt2deg.save_figure(save_file_name_,fig=fig, savefig=savefigure, dpi=fig_dpi)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb905683-82ad-4ef7-b5d5-9fbb6f524ce3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -312,7 +607,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/uploadpypi b/uploadpypi deleted file mode 100644 index 79c4aaf..0000000 --- a/uploadpypi +++ /dev/null @@ -1,2 +0,0 @@ -python -m bulid -python -m twine upload --repository mobilitypy dist/*