{
"cells": [
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0.8645785 -0.09823784j, 0.85954376-0.10151559j,\n",
" 0.85534282-0.10389085j, 0.85223139-0.10548823j,\n",
" 0.85032448-0.10640486j, 0.84968251-0.10670324j,\n",
" 0.85032448-0.10640486j, 0.85223139-0.10548823j,\n",
" 0.85534282-0.10389085j, 0.85954376-0.10151559j,\n",
" 0.8645785 -0.09823784j]),\n",
" array([0.58060059-0.38299298j, 0.5812387 -0.39895514j,\n",
" 0.58112332-0.41153593j, 0.58070618-0.42059005j,\n",
" 0.58031978-0.42604352j, 0.58016811-0.42786419j,\n",
" 0.58031978-0.42604352j, 0.58070618-0.42059005j,\n",
" 0.58112332-0.41153593j, 0.5812387 -0.39895514j,\n",
" 0.58060059-0.38299298j]))"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# matching the mixer LO input and RF output to 50 ohms using the S11/S22 parameters\n",
"# calculated using Qucs\n",
"\n",
"import qucs.qucsdata as qd\n",
"data = qd.QucsData('bfu520_mixer.dat').data\n",
"\n",
"\n",
"import numpy as np\n",
"# find the index closest to 915 MHz\n",
"freqs = data['frequency']\n",
"idx = np.abs(freqs - 915e+6).argmin()\n",
"\n",
"s11s = data['S[1,1]']\n",
"s22s = data['S[2,2]']\n",
"\n",
"s11s[idx,:], s22s[idx,:]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(407.8826087843155-328.42259033407754j)\n",
"834.9901269321724j\n",
"(672.324866234869+0j)\n",
"1.452380394909119e-07\n",
"407.8826087843155 152.45788592627554 133.76894422554054\n",
"5.712576904340215e-08 1.3003005258265486e-12 2.651849853388749e-08\n"
]
}
],
"source": [
"s11 = np.mean(s11s, axis=1)\n",
"s22 = np.mean(s22s, axis=1)\n",
"#print(s11)\n",
"\n",
"import skrf.network as net\n",
"\n",
"# conjugate match the source\n",
"z_s = net.s2z(np.array([[[s11[idx]]]]))[0, 0, 0]\n",
"print(z_s)\n",
"\n",
"# so it looks like it'll need a large series inductor to compensate the imaginary part, followed by a standard \n",
"# L match to 50 ohms\n",
"l_series_s = -np.imag(z_s)/(2*np.pi*915e+6)\n",
"\n",
"# how hard would it be to remove the imaginary part using a shunt element?\n",
"print(1j/np.imag(1/z_s))\n",
"print(1/(1/z_s + 1/(1j/np.imag(1/z_s))))\n",
"print(1/(np.imag(1/z_s)*2*np.pi*915e+6))\n",
"\n",
"# okay, 130 nH is pretty big so I'm going to go with the smaller series option\n",
"\n",
"r_s = np.real(z_s)\n",
"q_s = np.sqrt((r_s - 50.)/50.)\n",
"\n",
"x_1_s = r_s/q_s\n",
"x_2_s = q_s * 50.\n",
"print(r_s, x_1_s, x_2_s)\n",
"\n",
"# let's go with a shunt capacitor followed by an inductor\n",
"l_series_s2 = x_1_s/(2*np.pi*915e+6)\n",
"c_shunt_s = 1/(2*np.pi*915e+6*x_2_s)\n",
"print(l_series_s, c_shunt_s, l_series_s2)\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(407.8826087843155+0j) (39.61039510850523-120.77834197894384j) (39.61039510850523+31.679543947331695j)\n"
]
}
],
"source": [
"# double check\n",
"\n",
"z_1 = z_s + 2j*np.pi*l_series_s*915e+6\n",
"z_2 = 1/(1/z_1 + 1/(1/(2j*np.pi*915e+6*c_shunt_s)))\n",
"z_3 = z_2 + 2j*np.pi*915e+6*l_series_s2\n",
"\n",
"print(z_1, z_2, z_3)\n",
"\n",
"# TODO maybe simulate it using scikit-rf network stuff"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(89.46617486489218-105.51717177278026j)\n",
"1.83536387636225e-08 3.154815993634542e-08\n",
"(213.91402966297977+0j)\n",
"213.91402966297977 811.0129787222926 52.7523073675072\n",
"2.5782248342053757e-08 6.594586557283782e-12\n"
]
}
],
"source": [
"# time to conjugate match the output\n",
"#z_l = net.s2z(np.array([[[s22[idx]]]]))[0, 0, 0]\n",
"z_l = net.s2z(np.array([[[0.544 - 0.345j]]]))[0, 0, 0]\n",
"\n",
"print(z_l)\n",
"\n",
"# series inductor or shunt inductor to cancel out the imaginary impedance? let's compare values like before\n",
"z_series_l = -np.imag(z_l)/(2*np.pi*915e+6)\n",
"z_shunt_l = 1/(np.imag(1/z_l))/(2*np.pi*915e+6)\n",
"\n",
"print(z_series_l, z_shunt_l)\n",
"print(1/(1/z_l + 1/(2j*np.pi*915e+6*z_shunt_l)))\n",
"\n",
"# so either a pair of 15 nH series inductors or a 47 nH shunt; let's see how to the L section looks \n",
"# before deciding here\n",
"\n",
"#r_l = np.real(z_l)\n",
"r_l = np.real(1/(1/z_l + 1/(2j*np.pi*915e+6*z_shunt_l)))\n",
"q_l = np.sqrt((r_l - 200.)/200.)\n",
"\n",
"x_1_l = r_l/q_l\n",
"x_2_l = q_l * 200.\n",
"print(r_l, x_1_l, x_2_l)\n",
"\n",
"# let's give the smaller impedance to the capacitor, so it'll be a shunt inductor\n",
"l_shunt_l = x_1_l/(2*np.pi*915e+6)\n",
"c_series_l = 1/(2*np.pi*915e+6*x_2_l)\n",
"l_shunt_total_l = 1/(1/l_shunt_l + 1/z_shunt_l)\n",
"\n",
"print(l_shunt_total_l, 2*c_series_l)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}