{ "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 a single dot\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, 498.49it/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, 332.35it/s]\n", "Upgrading database; v5 -> v6: : 0it [00:00, ?it/s]\n", "Upgrading database; v6 -> v7: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 166.01it/s]\n", "Upgrading database; v7 -> v8: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 499.56it/s]\n", "Upgrading database; v8 -> v9: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 500.27it/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 three gates and one dot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "voltage_nodes = {\n", " 0: 'left_barrier',\n", " 1: 'plunger',\n", " 2: 'right_barrier',\n", "}\n", "charge_nodes = {\n", " 0: 'A',\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 the corresponding ground state dot occupation. Negative electron occupations are not allowed, they are replaces with zero." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0]\n" ] } ], "source": [ "qdot.V_v([\n", " -1.55, # left_barrier\n", " 1, # plunger\n", " -3.5, # right_barrier\n", "])\n", "\n", "# capacitances between gates and dots \n", "qdot.C_cv(\n", "# left_b plunger right_b\n", " [[-0.1, -1.3, -2]], \n", ")\n", "print(qdot.determine_N())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determine the voltage range required to sweep in order to measure specific charge states (dot occupancies)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(5.5038085937500085, 7.042281652688992)]\n" ] } ], "source": [ "N_limits = [(0, 2)]\n", "sweep_ranges = qdot.determine_sweep_voltages([1], N_limits=N_limits)\n", "print(sweep_ranges)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 1. \n" ] }, { "data": { "text/plain": [ "([],\n", " [None])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dataid = qdot.sweep_voltage(\n", " 1, \n", " sweep_ranges,\n", " n_steps=200,\n", " normalize=False,\n", ")\n", "plot_by_id(dataid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's change the voltage configuration to see what happens to the Coulomb peaks" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 2. \n" ] }, { "data": { "text/plain": [ "([],\n", " [None])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "qdot.V_v([\n", " -3, # left_barrier\n", " 1, # plunger\n", " -3.5, # right_barrier\n", "])\n", "dataid = qdot.sweep_voltage(\n", " 1, \n", " sweep_ranges,\n", " n_steps=200,\n", " normalize=False,\n", ")\n", "plot_by_id(dataid)\n" ] } ], "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 }