{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Aestimo Tutorial #2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we are going to consider hybridising of degenerate energy levels. This occurs when two or more systems which share some identical energy levels interact and this interaction causes these energy levels to shift apart from each other. This is called lifting of the degeneracy.\n", "\n", "To model this phenomenen, we will model two identical quantum wells as they are brought close together. There is a small problem though, in that the shooting wave method of finding energy levels used by aestimo doesn't work well with degenerate energy levels. So we will have to model the system at the point where the two quantum wells are already starting to interact and their levels hybridise.\n", "\n", "First though, we will model a single well on its own." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import aestimo.aestimo as solver\n", "import aestimo.config as ac\n", "ac.messagesoff = True # turn off logging in order to keep notebook from being flooded with messages.\n", "import aestimo.database as adatabase\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import copy\n", "from pprint import pprint" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "class Structure(object): pass\n", "s0 = Structure() # this will be our datastructure\n", "\n", "# TEMPERATURE\n", "s0.T = 15.0 #Kelvin\n", "\n", "# COMPUTATIONAL SCHEME\n", "# 0: Schrodinger\n", "# 1: Schrodinger + nonparabolicity\n", "# 2: Schrodinger-Poisson\n", "# 3: Schrodinger-Poisson with nonparabolicity\n", "# 4: Schrodinger-Exchange interaction\n", "# 5: Schrodinger-Poisson + Exchange interaction\n", "# 6: Schrodinger-Poisson + Exchange interaction with nonparabolicity\n", "s0.computation_scheme = 0\n", "\n", "# Non-parabolic effective mass function\n", "# 0: no energy dependence\n", "# 1: Nelson's effective 2-band model\n", "# 2: k.p model from Vurgaftman's 2001 paper\n", "s0.meff_method = 0\n", "\n", "# Non-parabolic Dispersion Calculations for Fermi-Dirac\n", "s0.fermi_np_scheme = True #needed only for aestimo_numpy2.py\n", "\n", "# QUANTUM\n", "# Total subband number to be calculated for electrons\n", "s0.subnumber_e = 3\n", "\n", "# APPLIED ELECTRIC FIELD\n", "s0.Fapplied = 0.00 # (V/m)\n", "\n", "# GRID\n", "# For 1D, z-axis is choosen\n", "s0.gridfactor = 0.1 #nm\n", "s0.maxgridpoints = 200000 #for controlling the size\n", "\n", "# REGIONS\n", "# Region input is a two-dimensional list input.\n", "# | Thickness (nm) | Material | Alloy fraction | Doping(cm^-3) | n or p type |\n", "s0.material =[\n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " [ 11.0, 'GaAs', 0, 2e16, 'n'],\n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " ]\n", "\n", "structure0 = s0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Initialise structure class\n", "model = solver.StructureFrom(structure0,adatabase) # structure could also be a dictionary.\n", " \n", "#calculate QW states\n", "result = solver.Poisson_Schrodinger(model)\n", "\n", "%matplotlib inline\n", "#solver.save_and_plot(result,model) # Write the simulation results in files\n", "solver.QWplot(result,figno=None) # Plot QW diagram\n", "solver.logger.info(\"Simulation is finished.\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Total layer number: 3\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total layer number: 3\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Total number of materials in database: 20\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total number of materials in database: 20\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 0.020046 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 0.020046 s\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Z+3P3l3PuZ2RkUFgBx8VjKBkZGXTp0oUlS5bkPvbWW2/x6quv8uWXX1KyZEkA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\nq1cX+NzsbPjiCxg7Fr78Epo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jaQUWafv37+fFF19k7ty5ALRq1YohQ4ZQsmRJ9u7dS7ly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\nJHjOs1/4K4HHDfv4ArfEE8iPT5486ZknzQ3xBMvjgQMHXnb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"text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Simulation is finished.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Simulation is finished.\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will model the double QW structure." ] }, { "cell_type": "code", "collapsed": false, "input": [ "s1 = copy.copy(s0) #simpler than redefining everything and changes to s0 should propagate to s1\n", "s1.material = [\n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " [ 11.0, 'GaAs', 0, 2e16, 'n'],\n", " [ 2.0, 'AlGaAs', 0.3, 0.0, 'n'], #barrier layer\n", " [ 11.0, 'GaAs', 0, 2e16, 'n'], \n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " ]\n", "barrier_layer = 2 # defines which layer will be adjusted later\n", "s1.subnumber_e = 6 # There will be double the number of energy states now.\n", "\n", "# Initialise structure class\n", "model1 = solver.StructureFrom(s1,adatabase) # structure could also be a dictionary.\n", "\n", "#calculate QW states\n", "result1 = solver.Poisson_Schrodinger(model1)\n", "\n", "#solver.save_and_plot(result,model)\n", "solver.QWplot(result1,figno=None)\n", "solver.logger.info(\"Simulation is finished.\")\n", "\n", "print 'state, Energy'\n", "print ' ,meV'\n", "for num,E in zip(range(result1.subnumber_e),result1.E_state):\n", " print '%5d %7g' %(num,E)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Total layer number: 5\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total layer number: 5\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Total number of materials in database: 20\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total number of materials in database: 20\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 0.0383871 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 0.0383871 s\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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3TodXtSpQq5asyE67XKWKcSWVyHlw4QCRjVRVhYuLi+6vu+++A155xbHTngMH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\n1kkrCFgnzXY87mnD/tMfgzQijThaox+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GjpXnLKsADQBKlgS6dQMWLQL++AN4+GGgWTPg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"text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Simulation is finished.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Simulation is finished.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "state, Energy\n", " ,meV\n", " 0 1011.17\n", " 1 1016.44\n", " 2 1083.6\n", " 3 1103.02\n", " 4 1202.29\n", " 5 1236.28\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the degenerate levels have split in energy. This same thing occurs in atoms and chemical bonds. In solids where there are huge numbers of atoms interacting in this way, there are so many split levels that we talk of energy bands instead of counting the levels directly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now simulate the system for various thicknesses of barriers between the two QWs. The shooting wave algorithm is weak at finding energy levels that are closely spaced and if they are too close together then it will miss both levels. However, the search algorithm can be made to look using any arbitrarily fine grid of energy values if we know that there are energy levels to be found. This will come at the cost of slowing the simulation. In this case, since we are not modelling self-consistant Poisson-Schrodinger effects (which are computationally more expensive due to their iterative nature) then this will not be much of a problem." ] }, { "cell_type": "code", "collapsed": false, "input": [ "q = 1.602176e-19 #C\n", "meV2J=1e-3*q #meV to Joules\n", "# Shooting method parameters for Schr\u00f6dinger Equation solution\n", "ac.delta_E = 0.005*meV2J #Energy step (Joules) for initial search. Initial delta_E is 1 meV. \n", "#ac.d_E = 1e-5*meV2J #Energy step (Joules) within Newton-Raphson method when improving the precision of the energy of a found level.\n", "#ac.E_start = 0.0 #Energy to start shooting method from (if E_start = 0.0 uses minimum of energy of bandstructure)\n", "#ac.Estate_convergence_test = 1e-9*meV2J" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "def set_barrier(d):\n", " \"\"\"Sets barriers between the two QWs to d (nm).\"\"\"\n", " model1.material[barrier_layer][0] = d\n", " model1.create_structure_arrays() # update the instance's internals\n", "\n", "results = []\n", "barriers = np.arange(1,11)\n", "for barrier in barriers:\n", " set_barrier(barrier)\n", " resulti = solver.Poisson_Schrodinger(model1)\n", " results.append(resulti.E_state)\n", "\n", "results = np.array(results)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.73137 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.73137 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.66888 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.66888 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.64143 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.64143 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.61989 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.61989 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.62761 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.62761 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.62602 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.62602 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.63055 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.63055 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.64965 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.64965 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.66582 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.66582 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "calculation time 1.68194 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:calculation time 1.68194 s\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value of ac.delta_E and the maximum value of the barrier have been chosen so that the all the levels are correctly found but increasing either will show that one or more of the lowest energy levels can easily be lost from the output of the simulation. A reminder that results should always be checked for sanity!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax1 = plt.subplot(111)\n", "for level in results.transpose(): ax1.plot(barriers,level)\n", "ax1.invert_xaxis()\n", "ax1.set_xlabel(\"barrier thickness (nm)\")\n", "ax1.set_ylabel(\"Energy (meV)\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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quRn4/vvAYfL9965rIv5CJCEBSE4Gxoxx3dLcRzAkiIh6i6IANTX+w+TsWeDE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\nTunZa+jd79577xXDhw8XBoN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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can how the energy levels split apart as the two quantum wells are brought closer together. This phenomena is often called level repulsion or anti-crossing (or non-crossing or avoided crossing) for quantum mechanical systems. However, it equally occurs with classical oscillators when we study the noraml modes of weakly coupled oscillators." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Anti-crossing Experiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In many anti-crossing experiments, the frequency of one oscillator is varied so that it crosses the frequency of the other oscillator. We can easily model such an experiment in our case in order to show the type of curves that are often found in the results of such experiments." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#reset delta_E\n", "ac.delta_E = 0.2*meV2J #Energy step (Joules) for initial search. Initial delta_E is 1 meV. \n", "\n", "s2 = copy.copy(s0) #simpler than redefining everything and changes to s0 should propagate to s1\n", "s2.material = [\n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " [ 11.0, 'GaAs', 0, 2e16, 'n'],\n", " [ 2.0, 'AlGaAs', 0.3, 0.0, 'n'], #barrier layer\n", " [ 9.0, 'GaAs', 0, 2e16, 'n'], \n", " [ 20.0, 'AlGaAs', 0.3, 0.0, 'n'],\n", " ]\n", "well2_layer = 3 # defines which layer will be adjusted later\n", "s2.subnumber_e = 6 # There will be double the number of energy states now.\n", "\n", "# Initialise structure class\n", "model2 = solver.StructureFrom(s2,adatabase) # structure could also be a dictionary.\n", "\n", "def set_well(d):\n", " \"\"\"Sets barriers between the two QWs to d (nm).\"\"\"\n", " model2.material[well2_layer][0] = d\n", " model2.create_structure_arrays() # update the instance's internals\n", " \n", "# turn off logging\n", "solver.logger.setLevel(\"WARNING\")\n", " \n", "#calculate QW states\n", "results2 = []\n", "well_thicknesses = np.linspace(8.0,14.0,200)\n", "for barrier in well_thicknesses:\n", " set_well(barrier)\n", " resulti = solver.Poisson_Schrodinger(model2)\n", " results2.append(resulti.E_state)\n", "\n", "results2 = np.array(results2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Total layer number: 5\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total layer number: 5\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Total number of materials in database: 20\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:aestimo:Total number of materials in database: 20\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "ax2 = plt.subplot(111)\n", "for level in results2.transpose(): ax2.plot(well_thicknesses,level)\n", "ax2.set_xlabel(\"2nd well thickness (nm)\")\n", "ax2.set_ylabel(\"Energy (meV)\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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GA2pqaqT1ampqYDAYut3O6tWru9TuS06GguPwzxcuwNfpvF2Dx4MP8/IwnY5C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\nXNetW4ctW7ZAoVBg48aNmD9/PgCgvLw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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how none of the levels cross but instead they seem to exchange characteristics as they pass by each other. If you cover the central part of the graph then this becomes even more obvious. This phenomena occurs in many branches of physics, when coupled oscillators are present. I call this anti-crossing, although apparently it is also called avoided crossing, intended crossing or non-crossing! (see [http://en.wikipedia.org/wiki/Avoided_crossing])" ] } ], "metadata": {} } ] }