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4 results for “parametric amplification”
Kerr reversal in Josephson meta-material and traveling wave parametric amplification datasets
<p>This repository contains raw data for results presented in article "Kerr reversal in Josephson meta-material and traveling wave parametric amplification" (Preprint : arXiv:2101.05815). All data is stored in numpy(numpy.org) array format.</p> <p><strong>Please site any usage to original publication.</strong></p> <p><br> # Gain data</p> <p> The data used for generating Fig. 3(a) of main text:<br> <br> - Gain_6_freq contains frequency axis, gain_6 contains corresponding gain data when the device is pumped at 6 GHz<br> <br> - Gain_8_freq contains frequency axis, gain_8 contains corresponding gain data when the device is pumped at 8 GHz<br> <br> - Gain_10_freq contains frequency axis, gain_10 contains corresponding gain data when the device is pumped at 10 GHz</p> <p><br> # Saturation data</p> <p> The data used for generating Fig. 3(d) of main text:</p> <p> - saturation_6_pow contains input signal power at 6.05 GHz and saturation_6_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p> - saturation_9.5_pow contains input signal power at 9.5 GHz and saturation_9.5_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p><br> # Noise data</p> <p> The raw data used for fitting noise performance of the TWPA as depicted in Fig. 4 of main text:</p> <p> - noise_without_TWPA_temperature : contains noise temperature of the amplification chain without TWPA, in<br> Kelvin.<br> - noise_without_TWPA_sys_gain_dB : contains gain of the amplification chain without TWPA, in dB.</p> <p> - noise_freq : contains frequency axis for the measured PSD.</p> <p> - noise_thermal_source_temperature : contains temperature data of the thermal noise source.</p> <p> - noise_with_TWPA_PSD_vs_thermal_source_temperature : contains PSD measured with 200 MHz RBW as a function<br> of frequency and temperature of thermal noise source.</p> <p><br> # Transmission data</p> <p> Normalized transmission through the device.</p> <p> - transmission_flux : contains quantized flux axis for the measured transmission.</p> <p> - transmission_freq : contains frequency axis for the measured transmission.</p> <p> - transmission : contains transmission as a function of frequency and quantized flux, in dB.</p> <p><br> # Dispersion data</p> <p> Dispersion through the device.</p> <p> - dispersion_freq : contains frequency axis for the measured dispersion.</p> <p> - dispersion_phase_PCB : contains phase accumulation when RF switch is in PCB position as a function of<br> frequency, in radians.<br> - dispersion_flux_mA : contains flux axis for the measured dispersion, in mA.</p> <p> - dispersion_phase_device : contains phase accumulation when RF switch is in device position as a function<br> of frequency and flux, in radians.</p>
Optical parametric amplification seeded by four-wave mixing in photonic crystal fibres
<p>Open access dataset for figures in 'Optical parametric amplification seeded by four-wave mixing in photonic crystal fibres', accepted for publication in Nonlinear Frequency Generation and Conversion: Materials and Devices XXI, paper 11985-4, SPIE LASE Photonics West, 2022.</p>
Dataset - In-situ amplification of spin echoes within a kinetic inductance parametric amplifier
<p>Raw data and analysis for arXiv:2211.11333</p> <p> </p>
Kerr nonlinearity and parametric amplification with an Al-InAs superconductor-semiconductor Josephson junction
<h1>Brief description</h1> <p><br>This repository contains data, code, and other materials for "Kerr nonlinearity and parametric amplification with an Al-InAs superconductor-semiconductor Josephson junction". Files are zipped by types (data, fabrication, measurement code and results) and experiments (Kerr nonlinearity measurements and parametric amplifier experiment).</p> <h1><br>Data formats:</h1> <p>.db: database file storing all measured data. QCoDeS version 0.39.1, QCoDeS github repo: https://github.com/microsoft/Qcodes. Please refer to 'plottr' tool to view the database files: https://github.com/toolsforexperiments/plottr . Plottr version: 0.0<br>.gds: GDSII pattern files for Litography<br>.ipynb: ipython notebooks that run measurements<br>.py: python scripts for generating the GDSII patterns or conducting measurements<br>.emz: AWR projects (model and data), AWR design environment version is 22.1<br>others: figures, etc..</p> <h1><br>File structure and instruction:</h1> <p>GDS patterns << GDS files for simulations and device fabrication<br> 4cavtunable_nocost_Q150-300_top-GND_bot-OP.gds << the GDS for device B, used in AWR simulation for BBQ model.<br> Distributed_JJFET_sim_v12.gds << the GDS for device A, used in AWR simulation for BBQ model.<br> Distributed_JJFET_sim_v12.py << the python script to generate the 'sim' GDS<br> Distributed_JJFET_v12.gds << the GDS pattern for fabrication (e-beam lithography)<br> Distributed_JJFET_v12.py << the python script to generate the 'fab' GDS<br>AWR_simulation << files of microwave simulations<br> Compare_P1db.ipynb << simulate P1dB of AlOx based JPA and Al-InAs based JPA<br> plots << Raw plots from Compare_P1db.ipynb<br> 202312 single JJ compression power.emz << AWR project for simulating P1dB of AlOx based JPA and Al-InAs based JPA<br> extra data << extra AWR data (user-defined format) to plot the gain versus pump frequency and power for Al-InAs based JPA - AWR tends to run out of system memory, therefore I manually divided the simulation task into small segements, and save data after each small tasks.<br> Series BBQ - device B.emz << AWR project to derive the BBQ model for device B<br> Series BBQ - device A.emz << AWR project to derive the BBQ model for device A<br>Code-Kerr-measurement<br> Compute and compare c4c2 << code that takes the measured Kerr into the c4/c2 ratio <br> Kerr fit device A.ipynb << code to analyze Kerr of device A, database file needed is jjfeta0425.db<br> Kerr fit device B.ipynb << code to analyze Kerr of device B, database file needed is 0722.db<br> Data_analysis_IMD.ipynb << code to analyze Kerr of device B using intermodulation spectroscopy, data needed is Data_IMD<br> Data_analysis_plots << Raw plots from data analysis codes<br>Code-Parametric_amp<br> Data_acquisition.ipynb << code to take measurements<br> Data_analysis_parametric_noise_ratio_plot.ipynb << code for analyzing the noise visibility ratio of the 4WM amp, database files needed are jjfeta0226.db <br> Data_analysis_parametric_amp.ipynb << code for analyzing the parametric amplification, including gain, bandwidth etc., database files needed are jjfeta0226.db, jjfeta0225.db, jjfeta0223.db, jjfetamp0113-3.db<br> Data_analysis_plots << Raw plots from data analysis codes<br>Data << raw data for analysis<br> Data-4WM_parametric_amplifier << data for the parametric amplifier<br> jjfeta0226.db <br> jjfeta0225.db<br> jjfeta0223.db<br> jjfetamp0113-3.db<br> Data-Kerr_measurement << data for the Kerr measurement (Stark shift)<br> jjfeta0425.db<br> 0722.db<br> Data_IMD << data for the Kerr measurement on device B (IMD)</p>
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