{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from qcodes.dataset.experiment_container import new_experiment\n", "from qcodes.dataset.experiment_container import load_by_id\n", "from qcodes.dataset.plotting import plot_by_id\n", "\n", "import nanotune as nt\n", "from nanotune.model.capacitancemodel import CapacitanceModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Capacitance model for quantum dots\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The system of electrostatic gates, dots and reservoirs is represented by a system of conductors connected via resistors and capacitors. Albeit classical and simple, the capacitance model explains and qualitatively reproduces relevant transport features of gate-defined quantum dots.\n", "\n", "The capacitance model is defined by a capacitance matrix $\\mathbf{C}$, whose elements $C_{ij}$ are the capacitances between individual elements $i$ and $j$. We distinguish between two different types of elements, the charge and the voltage nodes, representing quantum dots and electrostatic gates respectively. Each node $i$ is defined by its charge $Q_{i}$ and electrical potential $V_{i}$. For simplicity, we write charges and potentials on all nodes of the system in vector notation. We denote charges on charge and voltages nodes by $\\vec{Q_{c}}$ and $\\vec{Q_{v}}$ respectively, and electrical potentials by $\\vec{V}_{c}$ and $\\vec{V}_{v}$. The capacitance model allows to calculate potentials on voltage nodes resulting in the desired number of charges on charge nodes. We consider a system of $N_{c}$ charge nodes and $N_{v}$ voltage nodes.\n", "\n", "The capacitor connecting node $j$ and node $k$ has a capacitance $C_{jk}$ and stores a charge $q_{jk}$. The total charge on node $j$ is the sum of the charges of all capacitors connected to it, \n", "\\begin{equation}\n", "Q_{j} = \\sum_{k} q_{jk} = \\sum_{k} C_{jk} (V_{j} - V_{k}).\n", "\\end{equation}\n", "Using the vector notation for charges and electrical potentials introduced above, this relation can be expressed using the capacitance matrix, $\\vec{Q} = \\mathbf{C} \\vec{V}$. Distinguishing between charge and voltage node sub-systems, this relation becomes\n", "\\begin{equation}\n", "\\begin{pmatrix} \\vec{Q_{c}} \\\\ \\vec{Q_{v}} \\end{pmatrix} = \n", "\\begin{pmatrix}\n", "\\mathbf{C_{cc}} & \\mathbf{C_{cv}} \\\\\n", "\\mathbf{C_{vc}} & \\mathbf{C_{vv}}\n", "\\end{pmatrix}\n", "\\begin{pmatrix}\n", "\\vec{V_{c}} \\\\\n", "\\vec{V}_{v}\n", "\\end{pmatrix}.\n", "\\end{equation}\n", "Diagonal elements of the capacitance matrix, $C_{jj}$, are total capacitances of each node and carry the opposite sign of the matrix's off-diagonal elements.\n", "The off-diagonal elements of $\\mathbf{\\mathbf{C_{cc}}}$ are capacitances between charge nodes, while the off-diagonal elements of $\\mathbf{\\mathbf{C_{vv}}}$ are capacitances between voltage nodes. The elements of $\\mathbf{\\mathbf{C_{cv}}}$ are capacitances between voltage and charge nodes, and allow to calculate so-called virtual gate coefficients - useful knobs in semiconductor qubit experiments.\n", "\n", "\n", "Illustration of capacitances between gates and gates and dots: \n", "Each gate voltage $V_{i}$ will tune the number of charges on each dot. The capacitance of the dots, $C_{A}$ and $C_{B}$, are sums of all capacitances connects to $A$ and $B$ respectively. Gates located further away will have a smaller capacitive coupling. Most labels of capacitance between gates are omitted for readability. \n", "\n", "\n", "\n", "Names and layout indices of gates:\n", "Six electrostatic gates, three barriers and two plungers, are used to define two, possibly coupled, quantum dots. Barrier gates are primarily used to create potential barriers, while plungers are used to tune electron density and thus the dot's electrochemical potentials.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initiate or set database where data should be saved. When initializing a database a new qcodes experiment needs to be created as well." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Upgrading database; v0 -> v1: : 0it [00:00, ?it/s]\n", "Upgrading database; v1 -> v2: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 504.18it/s]\n", "Upgrading database; v2 -> v3: : 0it [00:00, ?it/s]\n", "Upgrading database; v3 -> v4: : 0it [00:00, ?it/s]\n", "Upgrading database; v4 -> v5: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 333.62it/s]\n", "Upgrading database; v5 -> v6: : 0it [00:00, ?it/s]\n", "Upgrading database; v6 -> v7: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 142.93it/s]\n", "Upgrading database; v7 -> v8: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 333.23it/s]\n", "Upgrading database; v8 -> v9: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 250.06it/s]\n" ] }, { "data": { "text/plain": [ "capacitance_extraction#capa_model#1@C:\\Workshop\\nanotune\\docs\\examples\\capa_model_test.db\n", "-----------------------------------------------------------------------------------------" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp_name = 'capacitance_extraction'\n", "sample_name = 'capa_model'\n", "db_name = 'capa_model_test.db'\n", "nt.new_database(db_name, '.')\n", "new_experiment(exp_name, sample_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize an instance of CapacitanceModel with six gates and two dots" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "voltage_nodes = {\n", " 0: 'top_barrier',\n", " 1: 'left_barrier',\n", " 2: 'left_plunger',\n", " 3: 'central_barrier',\n", " 4: 'right_plunger',\n", " 5: 'right_barrier',\n", "}\n", "charge_nodes = {\n", " 0: 'A',\n", " 1: 'B'\n", "}\n", "\n", "qdot = CapacitanceModel(\n", " 'qdot',\n", " charge_nodes=charge_nodes,\n", " voltage_nodes=voltage_nodes,\n", " db_name=db_name,\n", " db_folder='.'\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set voltages, define capacitance matrix and calculate ground state dot occupation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.0]\n" ] } ], "source": [ "qdot.V_v([\n", " -1, # top_barrier\n", " -1, # left_barrier\n", " -0.5, # left_plunger\n", " -1, # central_barrier\n", " -0.1, # right_plunger\n", " -1, # right_barrier\n", "])\n", "\n", "# off-diagonal entries of C_cc, ie.e inter-dot capacitance\n", "qdot.C_cc([[-2]])\n", "# # capacitances between gates and dots\n", "# top_b left_b left_p center right_p right_b \n", "qdot.C_cv([[-0.6, -0.6, -0.5, -0.5, -0.1, -0.1], # A\n", " [-0.5, -0.3, -0.2, -0.8, -0.5, -0.9]]) # B\n", "\n", "print(qdot.determine_N())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define which dot occupancies to probe and calculate respective voltage ranges of two gates to sweep." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(2.8260797502049875, 6.304308844864283), (3.8695883841630536, 6.478321143336265)]\n" ] } ], "source": [ "N_limits = [(0, 2), (0, 2)]\n", "sweep_ranges = qdot.determine_sweep_voltages([2, 4], N_limits=N_limits)\n", "print(sweep_ranges)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 1. \n", "Wall time: 42.8 s\n" ] }, { "data": { "text/plain": [ "([],\n", " [])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%time\n", "dataid = qdot.sweep_voltages(\n", " [2, 4],\n", " sweep_ranges,\n", " n_steps=[40,40],\n", " target_snr_db=100,\n", " add_noise=False,\n", " add_charge_jumps=False,\n", " normalize=False,\n", ")\n", "plot_by_id(dataid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sweep one voltage to show Coulomb oscillations" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 10. \n" ] }, { "data": { "text/plain": [ "([],\n", " [None])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "qdot.V_v([\n", " -1, # top_barrier\n", " -1, # left_barrier\n", " 3.2, # left_plunger\n", " -1, # central_barrier\n", " -0.1, # right_plunger\n", " -1, # right_barrier\n", "])\n", "\n", "dataid = qdot.sweep_voltage(\n", " 4, \n", " sweep_ranges[1],\n", " n_steps=200,\n", " target_snr_db=100,\n", " normalize=False,\n", " )\n", "plot_by_id(dataid)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 2 }