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@kanhua
Last active October 2, 2018 06:16
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Demo of Deep Level Fourier Transient Spectroscopy (DLFTS)
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulation of DLTS signal\n",
"This notebook simulates the conventional and Fourier DLTS signals."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from DLTSsimulator import trapLevel\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot a single capacitance transient\n",
"\\begin{align}\n",
"C(t,T)= \\Delta C_0 \\exp(-e_m(T)t)\n",
"\\end{align}\n",
"where the emission rate $e_m(T)$ is described by:\n",
"\\begin{align}\n",
"e_m(T)\\propto\\exp(-\\frac{qE_a}{kT})\n",
"\\end{align}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kanhua/Dropbox/DocumentGlacier/archive repo/dltskit/DLTSsimulator.py:53: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if t == \"default\":\n"
]
},
{
"data": {
"image/png": 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3PRDfKAIGB5wnFT4F/Kq/XyTdyj2pC3GnCzMrAn4G/K27Hw06z4Uys1uBRndf\nEXSWi5QFzAAedPfpwDHS48//MyTmo+cDY4DhQKGZ3R5sKunKzP6B+LTs4/39WulW7slcrDstmFk2\n8WJ/3N2fDjpPH80BbjOzbcSnyP7IzH4cbKQ+aQAa3P3UX0+LiZd9urkJ2OruTe7eDjwNvD/gTBdr\nn5kNA0jcNgacp8/M7A7gVuATl+Ia0+lW7slcrHvAMzMjPr+73t2/GXSevnL3L7t7jbuPJv7/4iV3\nT7stRXffC+w0s4mJRTcC6wKM1Fc7gNlmVpD4GbuRNPxguJslwB2J+3cAzwSYpc/MbC7w34Db3P34\npXjNtCr3xAcSpy7WvR54yt3XBpuqT+YAf058S/etxNctQYfKcH8DPG5mq4H3AP8z4DwXLPGXx2Jg\nJfAO8X/faXOEp5k9AfwemGhmDWZ2J/A14ANmtgn4QOLxgHaO9/HvQDHw68S/9+/1ew4doSoiEj5p\nteUuIiLJUbmLiISQyl1EJIRU7iIiIaRyFxEJIZW7iEgIqdxFREJI5S4iEkL/HwJo1s2Q4pxEAAAA\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f0406a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tp = trapLevel([0.005])\n",
"ttime = np.linspace(0, 10, 1000) / tp.emRateT(300)\n",
"_, ts1 = tp.getTransient(210, ttime)\n",
"plt.plot(ttime, ts1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kanhua/miniconda3/lib/python3.6/site-packages/matplotlib/pyplot.py:3259: MatplotlibDeprecationWarning: The 'hold' keyword argument is deprecated since 2.0.\n",
" mplDeprecation)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10f06d198>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10f050b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cn=np.fft.fft(ts1)\n",
"plt.plot(np.real(cn),hold=True,label=\"a\")\n",
"plt.plot(np.imag(cn),label=\"b\")\n",
"plt.ylabel(\"Fourier coefficients\")\n",
"plt.xlabel(\"ordrer n\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulate conventional DLTS\n",
"For conventional DLTS, we select two time points $t_1$ and $t_2$.\n",
"The dlts signal of each temperature point $T_k$ is then calculated by\n",
"\n",
"\\begin{align}\n",
"\\Delta C(t_2,t_1,T_k)= \\Delta C_0 \\exp(-e_m(T_k)t_2)- \\Delta C_0 \\exp(-e_m(T_k)t_1)\n",
"\\end{align}\n",
"\n",
"Assuming that we have two trap level states with activation energies 0.05 eV and 0.1 eV:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kanhua/Dropbox/DocumentGlacier/archive repo/dltskit/DLTSsimulator.py:53: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if t == \"default\":\n"
]
},
{
"data": {
"image/png": 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532TXrsQmbSzYtIB+nfuFraOjQuEPvzsSKeXIJg5lRBRtwlZZSf2BAxi760Oi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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1116d67b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tp1 = trapLevel([0.05],cap_rate=[1],delta_c=[0.01])\n",
"tp2 = trapLevel([0.1],cap_rate=[1],delta_c=[0.05])\n",
"tp3 = trapLevel([0.05, 0.1],cap_rate=[1,1],delta_c=[0.01,0.05])\n",
"\n",
"T = np.linspace(30, 500, num=500);\n",
"tp_arr=[tp1,tp2,tp3]\n",
"\n",
"dltsSig_list=[]\n",
"label_list=[\"Ea=0.05eV\",\"Ea=0.1eV\",\"Ea1=0.05eV,Ea2=0.1eV\"]\n",
"for tp_i,tp in enumerate(tp_arr):\n",
" dltsSig = tp.getConvDLTS(TRange=T, plotTransient=False)\n",
" dltsSig_list.append(dltsSig)\n",
" pass\n",
"\n",
"for i,d in enumerate(dltsSig_list):\n",
" plt.plot(d[:,0],d[:,1], '.',label=label_list[i])\n",
" \n",
"\n",
"plt.plot(dltsSig_list[0][:,0],dltsSig_list[0][:,1]+dltsSig_list[1][:,1])\n",
"plt.xlabel('temperature (K)')\n",
"plt.ylabel('simulated DLTS signal')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1117fa470>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6OL2dpO301/88lpnYv59JEo/S7H85A/dP/tvvPUW5w3P8Tyqf0gxUJ7yuAnb3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f06db38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tp1 = trapLevel([0.05],cap_rate=[1],delta_c=[0.01])\n",
"t,ct=tp1.getTransient(200)\n",
"plt.plot(t,ct)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit the Gaussian function to conventional DLTS\n",
"The DLTS of Ea=0.1eV looks very like Gaussian function, we can check."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11185cd30>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RtSKyTkTW+jsw0zc0l69nY3MW+b2god0rv9WyuycIj3CmSLEGd2NO4MutrUv9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\niTHGmG6xGokxxphusUTSC4nIcyKyX0T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"text/plain": [
"<matplotlib.figure.Figure at 0x11174e438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def gaussian(a_0,t,b,c):\n",
" return a_0*np.exp(-(t-b)**2/(2*c**2))\n",
"T=dltsSig_list[1][:,0]\n",
"plt.plot(T,gaussian(0.0217,T,191,30),label=\"Gaussian fit\")\n",
"plt.plot(T,dltsSig_list[1][:,1],label='Ea2=0.1eV')\n",
"plt.xlabel(\"temperature (K)\")\n",
"plt.ylabel(\"conventional DLTS\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result shows that, although conventional DLTS looks pretty much like Gaussian, the shape of conventional DLTS is skewed. In other words, it is not a symmetric distributaion, and therefore not Gaussian or Lorenzian."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the result of DLTFS\n",
"We plot the DLTFS using the same transients as we used in conventional DLTS.\n",
"In this calulation, we get the transients of each temperature and run discrete Fourier transform. Then we plot the Fourier coefficient b1 versus temperature. This is the process that is identical to our DLTS system."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kanhua/Dropbox/DocumentGlacier/archive repo/dltskit/DLTSsimulator.py:53: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if t == \"default\":\n"
]
},
{
"data": {
"image/png": 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VFyscb6HHVegxpmeC0IG+VuWYsM3NlpP/RROIbgH9D1l5IXopHRt2ZEKHCcVf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1117fab70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tp1 = trapLevel([0.05],cap_rate=[1],delta_c=[0.01])\n",
"tp2 = trapLevel([0.1],cap_rate=[1],delta_c=[0.05])\n",
"tp3 = trapLevel([0.05, 0.1],cap_rate=[1,1],delta_c=[0.01,0.05])\n",
"\n",
"# Setting of temperature range\n",
"T = np.linspace(30, 1000, num=500);\n",
"\n",
"dltsSig=np.zeros((T.shape[0],3))\n",
"\n",
"rateWindowList = [0.1,0.011]\n",
"\n",
"tp_list=[tp1,tp2,tp3];\n",
"\n",
"for tp_i,tp in enumerate(tp_list):\n",
" for i,t in enumerate(T):\n",
" _,trans=tp.getTransient(t,t=np.linspace(0,100,num=1000))\n",
" dltsSig[i,tp_i] = -np.imag(np.fft.fft(trans))[1]\n",
" pass\n",
" \n",
"\n",
"plt.plot(T, dltsSig[:,0], '.',label=\"Ea1=0.05eV\")\n",
"plt.plot(T, dltsSig[:,1], '.',label='Ea2=0.1eV')\n",
"plt.plot(T, dltsSig[:,2], '.',label='Ea1=0.05eV, Ea2=0.1eV')\n",
"plt.plot(T, dltsSig[:,0]+dltsSig[:,1],'-',label='sum of b1(Ea1)+b1(Ea2)')\n",
"\n",
"plt.xlabel('temperature (K)')\n",
"plt.ylabel('simulated DLTS signal (b1)')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fitting a Gaussian function to DLTFS\n",
"We then compare one of the DLFTS we plotted with a Gaussian function"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x111a74748>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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F0YodFbN0tsww5XQaVhRXkZdpLQQSiyWZnqZmpXG83sm68k7m8d2TqnnW5RuH\npnZUTNKAH6a27j9MdW09U7LSmjZmXw2T58G8t2KuvHCK64Pv666kdbxV7OgcOyoGacAPUytcgW1q\nVpoVmGK0JNOtT49ERvbr0bU8PlgVOzrHjopxGvDD1NffVDKgZxKDU5NjuiTTU15WGoXFVTicXRi0\n1jl2lNKAH46MMaworiQvKw0RiemSTE95WWkcOdHAJl8WRPFG59hRMU4DfhjaVXmMfTUnmufvY7Qk\n01Oe6/X4amcXJ43TOXZUjNOAH4YKPPP3EPMlmW4DeyUzJK1b1/P47jl2tGJHxSgN+GGo4JtKeneL\nZ0R6D2tDjC160p6pWWmsKK7E2ZU8Pniv2HFfjKVUlNOAH4ZWFFeSOzQNm02a1rCNoUVP2pOXlUbV\nsXq2VxzpeOe2tKzY0YuxVIzQgB9m9tUcp/jgMfKyXAuNxeCiJ+2ZmtUH6GI9vptejKVilAb8MPPl\nDiugTx/e19oQ41fYtpSRlsyAnkldz+O7eU3tvKu9fBXVNOCHmeU7DtArOZ4xA3s2bYzhK2xbEhHy\nstL4eudB3xc2b0v+HTT/L+DUXr6Kahrww4gxhmXbD3L6sDTs7vx9jF9h683pw/qw//AJdlQcPbkD\nZeTB6FnNt+kAropiGvDDSGllLeWHaskf4Urn6BW2Xp3hen2W7zhw8gfTAVwVQzTghxF3AJs+3BqY\n1CtsvRvSpxuDU5P5YpsfAr4O4KoYogE/jCzfcZD0lESGu+vvAb3C1rv84X35aufBrs2r05IO4KoY\noQE/TBhjWL7jINOH97HmzwG9wrYd00f0oeZ4A+vLq/1zQB3AVTFAA36Y2Lb/CAeOnCDfXY4JeoVt\nO9xlq8v8kccHHcBVMUEDfphYvt0KXNPc+Xu9wrZd6SmJjB6QwrLtfgr44H0Ad/GdGvRV1NCAHyaW\n7ThIRloyGWndrA16hW2Hpg/vS2FxFcfrHf45oLcBXAws/qHm81VU0IAfBhxOw1c7D2o6p5PyR/Th\nRIOTlSVV/juotwFczeerKKEBPwysK6/m8PEGTed0Ul6WdYGa3/L4bvl3WK+7J83nqyigAT8MfL61\nApGmC4o0neOblKR4sjN688V2P78+GXkw+xFapXb0giwV4QIW8EUkQ0Q+EZFNIrJBRO4I1Lki3Wdb\nK5gwqBd9eiRaG/SCK5/lD+/DurJDVB+r9++Bc+fDnAW0uiDro/v8ex6lgiiQPfwG4EfGmDHA6cAP\nRGRsAM8XkaqP1bNqVxVnn5re4h694MoXZ49Kx2lg6fYK/x/cWz6/ZDl8qEFfRaaABXxjzB5jzErX\n74eBTcCgQJ0vUi3bcQCngbOOEsOfAAAalUlEQVQ8A75ecOWz7IxUeiXH8+mWAAR8cF2QJc23LXtU\n8/kqIgUlhy8imcAk4Gsv990kIoUiUlhREaD/tGHssy0VpCTFkZ3Ru2mjzoHvM7tNOHNkXz7bWtH1\nZQ/bk5HnCvqetD5fRaaAB3wR6QG8BtxpjKlpeb8x5mljTK4xJjc9vWVaI7oZY/hsawVnjuxLnL3F\nW6Fz4PvsnFH9qDh8go17Wv15+ceM+yH/zhYbNeiryBPQgC8i8VjB/iVjzL8Cea5ItG3/EfbWHG+e\nv9c58DvN/fp9tjWA3xBn3A+j57TYqJU7KrIEskpHgL8Bm4wxfwjUeSLZZ668c6v8vc6B3ynpKYmM\nG9STT7fsD+yJ8u8AW3zzbVq5oyJIIHv4+cB/AueJyGrXz6yOHhRLPt68n1H9UxjYK7lpo5Zkdsk5\np/Zj5a5DVNf6uTzTU0YeXP8OpI9qvl0rd1SECGSVzhfGGDHGTDDGZLt+3gnU+SJN9bF6CooruWBs\nPy/3aklmZ50zKh2H0/hnUZT2ZOTBtx6ndeXOAg36KuzplbYh8unW/TichgvG9G9+x5p/gKMeLcns\nnOyM3vTuFs+STfsCfzKvlTto0FdhTwN+iHy4cR99eyQycbBHOWZpAax6kcaevaZ0fBZnt3H+6P4s\n2byfeocz8Cf0WrmDFfS1ckeFKQ34IVDX4OSzLRVcMKYfNptHaqDxgisAgUnXaElmJ8wY25/q2npW\nfFMZpBO2EfS1XFOFKQ34IVDwTSWHTzS0Tud4TokclwQTrwlNAyPUWaf2JTHOxgcbg5DWcZtxPwyd\n3mKj1uir8KQBPwQ+2rSPpHgb+SM85r/XKZFPWreEOM4c2ZcPN+7DmCAOeF9wf+tyTQ36KgxpwA8y\nYwwfbtzHGSPSSU7wWE5Pp0T2i5ljB1B+qDZwV91601a5pgZ9FWY04AfZht01lB+qZebYFukcrb/3\ni/PH9MMm8MGGIKZ1oKlcs+XCKRr0VRjRgB9ki9fuIc4mzDytv5d7tf7+ZPXpkcjkoam8v2Fv8E/e\nuHCKt6B/h5ZsqpDTgB9ExhjeXreb/BF96d0tofmdOiWy31w0biCb9x5mR8WR4J88dz7c8L6X9A5W\nyeZr3wt6k5Ry04AfROvKqymtrGX2hIGt79RFy/1m9viBiMDiNXtC0wB3eqfVQC6w7hV49mKdcE2F\nhAb8IHp73R7i7cKFYwc0v0MrdPxqQK8k8jLT+Pfa3cGt1vHkHshtVbKJNffOMxdqXl8FnQb8IDHG\n8PbaPeSP6Euvbi16fsVLwXECq0LHqRU6fnDJxFPYvv8IW/YdDl0jMvLg+ndh/FWt7zNOHcxVQacB\nP0jWllVTVlXL7PFtpHOMazoA47Ruq5Ny8bgB2G3Cv9fsDnVT4Nt/8X5Frg7mqiDTgB8kb63ZTbxd\nmNkynQOuHr37rbBpD98P+vRIZPrwPvx7zZ7QpXU8zbgf5jxKq1k2QQdzVdBowA+CeoeTN1eXc/7o\n/q3TOWD16G0215QKiTpg6yeXTDyFXZXHWFNWHeqmWHLnw5wFeP1vt+4VWDBeUzwqoDTgB8FnWyo4\ncKSOb08e3PpOHbANmAtPG0BinI1/rSwLdVOauMs2vQ3mHtplpXi0ikcFiAb8IHhtZRl9uidwzigv\ni7Sv+Qc0HEenVPC/XsnxXDRuAG+sKud4vaPjBwRLe4O5oFU8KmA04AdY1dE6lmzaz6XZg4i3t3i5\ndf77gLsqN4Oa4w3BnUHTV20O5uKq4rkDFl2rvX3lNxrwA+zfa3dT53Dy7cmDWt+p898H3LRhfRjU\nO5l/FpaGuinezbgfbvjQe4oHYPNi+NtMreRRfqEBP8BeKypjzMCenHZKr9Z3ek6YZk/Q+e8DwGYT\n/l/uYL7YfoCyqmOhbo537hRPW1U8GKuSR3P76iRpwA+gdWXVrCmr5qpcL4O1jXTCtED7ds5gjIHX\nispD3ZT2tVfFA1ZuX3v76iRowA+g578spluC3Xt1DuiC5UGSkdaNM0b05eUVu2gIxnq3J8NdxTN6\ndhs7uHr7WsKpuiBgAV9EnhGR/SKyPlDnCGdVR+t4a81uLp80iJ5JXmrvdcA2qP5z2lB2Vx/nw3Ac\nvG0pIw+u/nv7uX13CeeTZ2iaR/kskD38hcBFATx+WHulsJQTDU6um5bpfYfipeBscN3QAdtAu2BM\nfwanJvPs8uJQN8V3Heb2gX3r4G8z4KkzYPEPNfirdgUs4BtjPgcqA3X8cOZwGl78uoS8rDRGDUjx\nvpPn/DkYGJAdtPbFIrtNmDctk4JvKtmwO0yuvPVV7ny44YO2e/sAe9dB4TOa41ft0hx+AHyyeT+l\nlbXMa6t3D7B3jccNnT8nGK7KzSA53s7CZcWhbkrnuXv7N3wIA8a3s6Mrx//wqVrDr1oJecAXkZtE\npFBECisqKkLdHL/48+c7GNQ7uY1lDPHI37vY4zV/HwS9usXz7cmDeHPNbg4cORHq5nRNRh7c/IWV\n5uk1pO39juxz1fDP0MCvGoU84BtjnjbG5BpjctPTvUw9EGFWFFeyoriKG8/Man1lrVtjdQ5o/j64\nrs/Pot7h5Jkvvgl1U05O7nz44Tor8Pf1spyiJ3fg1zr+mBfygB9tnvp0B6nd4vnOlAzvO7SsztEL\nroJqeHoPZo8fyHPLizl0rC7UzTl5ufPh1oL2K3rcSpZbgf+xSfCX87SsMwYFsizzH8CXwCgRKROR\nGwJ1rnCxaU8NSzbvZ/70LLolxHnfSatzQu6280ZytM4R+b18T545/o4Cf+VOKC+yyjofnaTVPTGk\njah08owxcwN17HD1+w+2kpIUx/zpmW3vpNU5ITdqQAoXjxvAs8uLueHMYfRK9nKdRKRyB/7SAmvw\ndvPb7e9ftRMKd1oVPgPGw+ApMHGudkKilKZ0/GR16SE+2rSPm84c5n2REzetzgkLt543gsPHGyKz\nYscXnhdvjZ4N3dsoIPDUWNqpdf3RKmA9/Fjz+w+2kNY9gevPyGp7J63OCRunndKLC0/rz9Of7+Ca\nqUNIT0kMdZMCwx34wcrZr3oeavbA4Q7W+t27rukDIG0YJKfCpOusMQMVsbSH7wefbNnP0m0HuOWc\n4fRIbOczVKtzwsp/XzSaEw1OFny0NdRNCY7c+fC9j+FHm3yr7nHzzPk/lAlPTNUB3wglYbHAs0tu\nbq4pLCwMdTM6pd7h5MIFn2MMvH/nWSTEtfEZWloAC2eDw1UZYk+E+Ys14IfYL97awAtflfDeHWcy\nsn8bV0VHM3euf886qN7Vuccmp0JiT+g1GNJHae4/RESkyBiT68u+2sM/Sc9/WcLOiqP8fM6YtoM9\naHVOmLr9/JF0S7Bz31sbCKfOT9C4Uz4/XNeU72/vgi5PtVVwqARKljXl/rXkM6xpDv8k7KmuZcGH\nWzn71HTOHdWv/Z2P12h1ThhK657APReN5udvrOeN1eVcPqm9tQuinGe+393zP7AdnPVWWscX7v3K\ni2DJL6xvAEk9retNdAwg5DTgd5Exhp+9vp56p5NfXjoOkTZmMwTrP8+Xj3tsEK3OCSPX5g3htaIy\nfrV4E+eO6kfvbgmhblLoeQZ/aPoAKC2Eoz5OMV1bZf24lRfBZw9ZU4GLWGWg+XfoN90g0oDfRW+t\n2c2Szfv52ewxDOnTrf2dm61dC9jsWp0TRmw24bdXjOeSP37Bz9/cwB/nTgp1k8KPt97/nnXWmNSR\nvb4fx7M66FCJNe1D2jBwNFgfAjoeEFAa8LugrOoYP39jPdkZvbk+v50yTLfjNTRbwnDarfrHHGbG\nDOzJnReM5OEPtnLBmH5cmu1l0Xllaav339n0j5vn/p5jAvpB4Hca8DupweHkzkWrcRp49Ops7LZ2\nUjlgDV4tf8xjg1g5TRV2bj57OJ9sqeBnb6wnZ0gqGWkdfHNTlrY+APass4K1o77jun9v2vsgsMVD\nXALUVkNCN5j6fR0f8IGWZXbSg+9u5qnPdvDo1dkd9wJLC6wZChurc7Dyl9e/q72UMLXr4DHm/HEp\ng1O78dr3p5OcYA91k6KD+6IvR50VpOuOQK2f10dyl4km9bTOESPfDDpTlqkBvxNeX1XGD19ewzVT\nh/Cby9tbhMJl6e9hyS9pTOeIDWY/oj2RMPfJlv18d+EKLssexB+umtj+gLzqupYfAp0dD+isHgMg\nLrH5B0IUVBB1JuBrSsdHhcWV/Pdr6zh9WBr3f+s03x7UMnc//faI/aOKJeeO6sddF5zK7z/cSkZq\nMnfN9PGKVNU5ufNb/3/wHA9wp2z89UHQ3jFaVhAl9YSGuqY2RElVkQZ8H6wvr+b6Z1cwuHcyf7p2\nctsLm3gqLYDlf/TYoLn7SHLreSMoq6rlsY+3k56SyH+2t1yl8p+W4wFuLT8I3MH48H7fy0Q70tE4\ng7uqqEd/6NGv+beElh8OYfrNQQN+BzburuG6ZwromRzPizdOJa27jzXayx4F41GKKTYtxYwgIsKv\nLx/HwaN1/PzNDQAa9EOprQ8CaD1I7E7ZOOqspR7xc9r6yD7XcX3g7ZuDtw+KIH04aMBvx4riSr67\ncAU9EuN48capnNI72bcHFi5sPQ/5qIsi+qtgLIqz23ji2kn84KVV/PzNDRw54eDms4dpTj/cdPRh\nsObvULEVDpW2DrpdrSDqDF+PX15k/RvAoK8Bvw2vryrj3tfWMah3Mi/cOJVBvgb70gJ4+y6a9SrE\nDvl3BqSdKrAS4+z86doc7nplNQ+9t5nt+4/wmyvGkRin1TsRISOv445Wy8Fjb2maQFQVebPpTQ34\nwXS83sGD725m4fJiTh+WxhPX5NCnRyfmSv/ovuapHARm/0F79xEsIc7GY1dPYkS/Hiz4aBs7Dxzh\nsasnaZ1+tPA2eOxN4UL46k/QcLzt1MzJfnMYc2nnH9MJWpbpYdWuKn70zzXsrDjK9fmZ/HTWGN8G\naN0+vM/KJXoaPbvtr5sq4ryzbg/3vLoWpzH8bPZY5uZlaIpHedfWNwc/5/C1Dr+T9h8+ziMfbuPl\nFbsY0DOJ3105kTNG9u3cQbwFewRu+EB791GmrOoY//3aWpZtP8iUzFR+OmsMk4akhrpZKkZpHb6P\n9tcc57kvi3l2WTF1DU6um5bJXTNPpWdSJxe1fu17sO6V1tsjvGZXeTc4tRsvfHcqrxSW8vAHW7n8\nT8uZNX4AN589nAmDe4e6eUq1KeYCvtNp+Oqbg7xaVMa/1+ymwWm4eNwA7r5wNFl9u3fuYKUFVs6+\nZHnr+/LvhBn3+6fRKuzYbMLVeUOYM/EUnv5sB88uK+addXvJy0xj7tQMZo4dQPf2lrtUKgQCmtIR\nkYuARwE78FdjzIPt7R+olM7h4/V8ueMgX2w/wAcb9rG35jjdE+xcOXkw1+dnkdnZQO8evDmwFa81\nvhrsY87h4/W8UljGwuXfUFpZS3K8nXNHp3P2qemcMTLd9yovpTopLHL4ImIHtgIzgDJgBTDXGLOx\nrcecbMA3xnDwaB0lB4+xdd9h1pVXs768mg27a3A4Dd0S7Ewf3pdLs0/hgjH9fZ8Yy3N0vqPR9/FX\nwbf/0uXnoCKbMYaikipeX1XOR5v2sa/mBABD+3RjwuDejB/Uk7EDe5HZtxun9ErG1tFsq0p1IFwC\n/jTgF8aYC123fwJgjPltW4/pSsB3OA2LnnyAyZWLcTacoLvzKALUmO4kxTVgtyfS215LUryNhO5p\n2E+0MVruj/pb7dkrD8YYtu0/wudbKygsrmJdeTXlh2ob70+IszEkrRsDeyWRnpJIeo9E+vZIJLV7\nAj0S7XRPjKN7Yhw9XP8mx9uJtwvxdhtxNsFuE60QUmEzaDsIKPW4XQZM9fdJ7Cuf45qKP1g3BCt5\nBMABa5MDcAB1wNFyf5/edV6dBVO1JiKc2j+FU/uncKNrVo2DR06wZe9hig8eo+TgUYoPHmVvzQl2\nVhyl4sgJ6hqc7R+0hXi7EGezNX4QiAgi1n8F61/P2+Jql/f7BMB1O1JF6gdgWrcEXrl5WsDPE8iA\n7+2Vb/V1QkRuAm4CGDJkSOfPsunN0P2Bpg6D4edE9Vzbyr/69Ehk+ohEpo9ofZ8xhprjDRw6VsfR\nEw6O1jVw5EQDR10/x+ocNDgM9U4nDQ5Dg8NJvdP1r8NQ73DiNAAGY7B+3L/TdJvG28Zje9PtiBXB\njU9JCs4AfyDPUgZkeNweDLRKfhtjngaeBiul0+mzjLkUdnzcxSZ2knuWvDCcBU9FPhGhV3I8vZI7\nWRaslI8CGfBXACNFJAsoB64GrvH7WdxBt6O5MDrK27f3uCiYB1sppQIW8I0xDSJyK/A+Vmb9GWPM\nhoCczNe5MJRSKoYFNHFkjHkHeCeQ51BKKeWbTswMppRSKpJpwFdKqRihAV8ppWKEBnyllIoRGvCV\nUipGhNUCKCJSAZS02NwX9zwJsUefe2yK1eceq88bTu65DzXGpPuyY1gFfG9EpNDXiYGijT53fe6x\nJFafNwTvuWtKRymlYoQGfKWUihGREPCfDnUDQkife2yK1eceq88bgvTcwz6Hr5RSyj8ioYevlFLK\nD8I64IvIRSKyRUS2i8i9oW6PP4lIhoh8IiKbRGSDiNzh2p4mIh+KyDbXv6mu7SIij7lei7UikhPa\nZ3DyRMQuIqtEZLHrdpaIfO167i+LSIJre6Lr9nbX/ZmhbPfJEpHeIvKqiGx2vf/TYuV9F5Efuv7e\n14vIP0QkKVrfdxF5RkT2i8h6j22dfp9FZJ5r/20iMu9k2hS2Ad+1CPoTwMXAWGCuiIwNbav8qgH4\nkTFmDHA68APX87sXWGKMGQkscd0G63UY6fq5CXgy+E32uzuATR63HwIecT33KuAG1/YbgCpjzAjg\nEdd+kexR4D1jzGhgItZrEPXvu4gMAm4Hco0x47CmTb+a6H3fFwIXtdjWqfdZRNKA+7CWh80D7nN/\nSHSJMSYsf4BpwPset38C/CTU7Qrg830TmAFsAQa6tg0Etrh+/zMw12P/xv0i8QdrBbQlwHnAYqwl\nMQ8AcS3ff6w1Faa5fo9z7Sehfg5dfN49gW9atj8W3nea1rlOc72Pi4ELo/l9BzKB9V19n4G5wJ89\ntjfbr7M/YdvDx/si6INC1JaAcn1VnQR8DfQ3xuwBcP3bz7VbtL0eC4B7APeq3X2AQ8aYBtdtz+fX\n+Nxd91e79o9Ew4AK4FlXOuuvItKdGHjfjTHlwMPALmAP1vtYRGy8726dfZ/9+v6Hc8D3aRH0SCci\nPYDXgDuNMTXt7eplW0S+HiIyB9hvjCny3OxlV+PDfZEmDsgBnjTGTAKO0vS13puoee6uVMSlQBZw\nCtAdK5XRUjS+7x1p67n69TUI54Dv0yLokUxE4rGC/UvGmH+5Nu8TkYGu+wcC+13bo+n1yAe+JSLF\nwCKstM4CoLeIuFdh83x+jc/ddX8voDKYDfajMqDMGPO16/arWB8AsfC+XwB8Y4ypMMbUA/8CphMb\n77tbZ99nv77/4RzwGxdBd43aXw28FeI2+Y2ICPA3YJMx5g8ed70FuEfi52Hl9t3br3ON5p8OVLu/\nGkYaY8xPjDGDjTGZWO/rx8aYa4FPgCtdu7V87u7X5ErX/hHZ0zPG7AVKRWSUa9P5wEZi4H3HSuWc\nLiLdXH//7uce9e+7h86+z+8DM0Uk1fUNaaZrW9eEelCjgwGPWcBWYAfwP6Fuj5+f2xlYX83WAqtd\nP7OwcpRLgG2uf9Nc+wtW1dIOYB1WpUPIn4cfXodzgMWu34cBBcB24J9Aomt7kuv2dtf9w0Ld7pN8\nztlAoeu9fwNIjZX3Hbgf2AysB14AEqP1fQf+gTVWUY/VU7+hK+8z8F3Xa7AduP5k2qRX2iqlVIwI\n55SOUkopP9KAr5RSMUIDvlJKxQgN+EopFSM04CulVIzQgK/CgmsGyVtC3Q5fiMidItItgMcf6DGD\n6Dnu3123fyUi77tmklwkIiMD1Q4VfTTgq3DRGwiLgO+6+KW9/xt3Ap0K+B5XkvriLuAvXo7xP1hX\nKV9mjDmBNaPiPZ1ph4ptGvBVuHgQGC4iq0Xk/wBE5G4RWeGaH/x+17ZMseaR/6trTvWXROQCEVnm\nmi88z7XfL0TkBRH52LX9e+4TtXPcTSLyJ2AlkCEiT4pIoVjzt7v3ux1rHphPROQT17YjHse+UkQW\nun5fKCJ/cO33kIh0F2uO9BWuidMubeO1+DbwnucGEfkR1oV5lxhjal2blwIXdPLDRMUw/UNR4eJe\nYJwxJhtARGZizQ2eh3UV4lsichbW5fkjgP+HNW/4CuAarCuXvwX8FLjMdcwJWGsNdAdWicjbwLh2\njjsK60rGW1xt+B9jTKVrbYYlIjLBGPOYiNwFnGuMOeDD8zoVuMAY4xCR32BND/BdEekNFIjIR8aY\no+6dRSQLaw74Ex7HyHe1bbIxpvHDxRjjFJHtWHPqe05Ep5RX2sNX4Wqm62cVVo97NFagBmsCrnXG\nGCewAWtBCYN1SXqmxzHeNMbUugLzJ1hBvr3jlhhjvvJ4/FUistK172lYC/F01j+NMQ6P53SviKwG\nPsWaOmBIi/0HYk2f7Gk71ofTTC/H34/1jUOpDmkPX4UrAX5rjPlzs43W2gGevV+nx20nzf+mW84b\n4p5utq3jtuxp/xiYYoypcqVpktpoq+d5Wu5z1ON3Ab5tjNnSxnEAar0cYx9wLda3jIPGmE9anK8W\npXygPXwVLg4DKR633we+61ovABEZJCL9vD6ybZeKtWZqH6xJ2lZ04rg9sYJ1tYj0p/m87S3buk9E\nxrgGei9vpz3vA7e5ZopERCZ52Wcrzb+lAGCM2QpcAbwoItked52K9S1HqQ5pD1+FBWPMQdfA63rg\nXWPM3SIyBvjSFR+PAP8BONo7TgsFwNtYaZNfGmN2A7t9Oa4xZo2IrMIKpjuBZR53Pw28KyJ7jDHn\nYo0/LMZamWg90KON9vwSa97/ta6gXwzMaXHeoyKyQ0RGGGO2t7hvhYhcjzXucK6r7bUmcqdLVkGm\ns2WqqCQivwCOGGMeDnVbOktELscaoP1ZB/v9EKgxxvwtOC1TkU57+EqFGWPM6640VEcOYc0pr5RP\ntIevlFIxQgdtlVIqRmjAV0qpGKEBXymlYoQGfKWUihEa8JVSKkZowFdKqRjx/wGogl+MNU5OZAAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11184ad30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#t=np.linspace(0,800,num=100)\n",
"\n",
"plt.plot(T,gaussian(6.3,T,310,85),label=\"Gaussian fit\")\n",
"plt.plot(T,dltsSig[:,1], '.',label='Ea2=0.1eV')\n",
"plt.xlabel(\"temperature (K)\")\n",
"plt.ylabel(\"b1\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that a Gaussian function is definitely not a good estimate for DLFTS signal. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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}
import numpy as np
from scipy.constants import e, k
import matplotlib.pyplot as plt
e_over_k = e / k;
class trapLevel():
"""A class that generates the capacitance transient.
It is essentially a exponential transient generator.
However, it uses physical parameters as input and variable names"""
def __init__(self, activeE, cap_rate=1,delta_c=1):
"""activeE: a list of activation energies of traps in the units of eV.
cap : capture rate (implementation of capture is still under development)"""
self.activeE = np.array(activeE)
def set_param(param):
if isinstance(param,int) or isinstance(param,float):
return np.ones((len(activeE),))*param
elif isinstance(param,list):
return np.array(param)
self.capRate = set_param(cap_rate)
self.delta_c=set_param(delta_c)
def emRateT(self, T=300):
"""output an array of emission rates based on the
set temperture T and activation energy array
T: temperature in Kelvins"""
self.emRate = np.exp(-self.activeE * e_over_k / T)
return self.emRate
def getTransient(self, T, t="default", plotGraph=False, gridnum=1000):
"""Generate capacitance transients
T: temperature in Kelvins
t: an array of time. Set none if using default
gridnum: number of points of t"""
emRate = self.emRateT(T)
# define the contributions of exponential components
self.expContri = list()
if t == "default":
defaultFraction = np.linspace(0, 10, num=gridnum)
t = defaultFraction / emRate[0]
transientSum = np.zeros((len(t),))
for emIndex, em in enumerate(emRate):
tmpVal = self.capRate[emIndex] * self.delta_c[emIndex]*np.exp(-em * t)
self.expContri.append(tmpVal)
transientSum = transientSum + tmpVal
self.time = t;
self.trans = transientSum
if plotGraph == True:
self.plotTransient(T)
return t, transientSum
def plotTransient(self, T):
plt.plot(self.time, self.trans)
plt.xlabel('time (s)')
plt.ylabel('simulated signal')
plt.title('%s K' % str(int(T)))
plt.savefig('./output/transient %s K.png' % str(int(T)))
plt.close()
def getConvDLTS(self, rateWindow=0.5,
shift=None,
TRange=np.linspace(100, 300, num=50),
ttime=None, plotTransient=False):
"""
Calculate the transient(t1)-transient(t2).
t1 and t2 are determined by the parameters shift and rateWindow
t1=shift*len(ttime)
t2=(shift+ratewindow)*len(ttime)
return: transient(t1)-transient(t2)
"""
if shift == None:
shift = 0.2
if ttime == None:
ttime = np.linspace(0, 100, 1000) / self.emRateT(500)[0]
dltsSig = np.zeros((len(TRange), 2))
ttimelen = len(ttime)
for idx, temper in enumerate(TRange):
time, trans = self.getTransient(temper, ttime, plotGraph=plotTransient)
dltsSig[idx, 0] = temper
# This line needs to be fixed, this rate window implementation is bad
dltsSig[idx, 1] = trans[int(ttimelen * shift)] - trans[int(ttimelen * (shift + rateWindow))]
return dltsSig
def plotTransients():
tp = trapLevel([0.005])
ttime = np.linspace(0, 10, 1000) / tp.emRateT(300)
_, ts1 = tp.getTransient(210, ttime)
_, ts2 = tp.getTransient(300, ttime)
_, ts3 = tp.getTransient(350, ttime)
plt.plot(ttime, ts1, ttime, ts2, ttime, ts3)
np.savetxt("testout.csv",np.vstack((ttime,ts1)).transpose())
plt.show()
if __name__ == "__main__":
#gen_dlts_plot_emrate()
#testFit()
#gen_dlts_plot()
#plotTransients()
pass
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