Odmactor (ODMR Actor)

ODMR software SDK integrating functions of ODMR detection and spin manipulation.

This project is on continuous updating & using ...

Last updating date: April, 2022
Current implementation: ODMR detection

Design Method

Measurement modes and scheduling strategies

1. Frequency-domain detection
   Scan MW frequencies
   • Continuous-Wave ODMR
     1. polarize spin systems
     2. operate continuous MW and readout signals for the whole sequence period
   • Pulse ODMR
     1. polarize spin systems
     2. apply a fixed-interval MW pulse
     3. readout final fluorescence signals
2. Time-domain detection
   Scan time intervals
   • Ramsey detection
     1. initialize spin to ground state
     2. initialize spin to equal-amplitude superposition state using calibrated MW $\frac{\pi}{2}$ pulse
     3. wait for a time interval $\tau$
     4. operate a calibrated MW $\frac{\pi}{2}$ pulse again
     5. readout final spin state (population)
   • Rabi oscillation
     1. initialize spin to ground state
     2. operate a short MW pulse (100~200 ns)
     3. readout final spin state (population)
   • T1 relaxation
     1. initialize spin to excited state using calibrated MW $\pi$ pulse
     2. wait a time interval $\tau$
     3. readout final spin state (population)
   • Hahn echo Sequences are similar with those for Ramsey detecting, while there is an additional MW π pulse between the two π/2 pulses.
   • High-order dynamical decoupling Sequences are similar with those for Ramsey detecting, while there are N additional MW π pulse between the two π/2 pulses.
3. Spin manipulation
   Currently not implemented

The above specific scheduling methods could be abstracted into different detection sequences in experiments. They are all controlled in precision of "ns".

Thus the series of ODMR measurement experiments is simplified by a "pipeline". CW and Pulse ODMR could be used to characterize environmental physical quantities, while they also could be used to calibrate energy gap or fine structures. Ramsey detecting is usually used to characterize T2* (dephasing time) of spin systems. Some typical results of them are like the following figure.

OOP Classes implementation

general scheduling methods

• scheduler.config_odmr_seq(): configure ASG control sequences for laser, MW and tagger
• schedulel.stop(): stop all hardware (ASG, MW, Tagger) scheduling
• schedulel.close(): release instrument (ASG, MW, Tagger) resources
• schedulel.save_result(): save detailed measurement result into a ".json" file

necessary data fields of schedulers

• scheduler.channel: a dict instance in Python, presenting ASG channels to output control sequences
• scheduler.tagger_input: a dict instance in Python, presenting Tagger input channels (signal, trigger, etc.)
• scheduler.result: a list instance in Python, consisting of aggregated (avg or mean) measurement result
• scheduler.result_detail: a dict instance in Python, consisting of detailed measurement result
• scheduler.pi_pulse: {'freq': ..., 'power': ..., 'time': ...}, consisting of configuration information of the calibrated MW $\pi$ pulse
• scheduler.sequences: rational ASG sequences data in form of lists
• scheduler.sequences_figure: visualized ASG control sequences

specific scheduling methods

• scheduler.run_scanning(): for FrequencyDomainScheduler and TimeDomainScheduler, scan time intervals and MW frequencies, respectively
• scheduler.run_single_step(): for FrequencyDomainScheduler and TimeDomainScheduler, run a single setting point
• scheduler.set_delay_times(): for TimeDomainScheduler, this function should be called to design time interval scanning points
• scheduler.set_mw_freqs(): for FrequencyDomainScheduler, this function should be called to design MW frequency scanning points

Hardware support

General ODMR platform

Specific hardware used

Most general hardware resources are supported by our Odmactor programs. Below are some essential instruments usde in a systematic ODMR platform and directly controlled by our programs currently used in our QIM group.

Arbitrary Sequence Generator (ASG)

This is the most important instrument to synchronize control measurement processes.

• Vendor: CIQTEK
• Type: ASG8005
• Feature: 8 control channels

Microwave instrument (MW)

• Vendor: R&S
• Type: SMB 100A

Time Tagger (Tagger)

This is a T/D convertor as well as A/D convertor necessary for data acquisition.

• Vendor: Swabian
• Type: Time Tagger 20
• Feature: 8 detecting channels, 34 ps jitter, 8 M tags/s

Lock-in Amplifirer

Lock-in is a mature manner to detect weak signals, and this device manufactured by Standford Research is a usual lock-in amplifier.

• Vendor: Stanford Research
• Type: RS830
• Feature: 1 mHz ~ 102.4 kHz range, 0.01 degree resolution

Data Acquisition board

NI DAQ device is used when using lock-in detecting mode instead APD with Time Tagger mode.

• Vendor: National Instruments

Usage method

Basic steps

• Configuration
  1. Set channels of ASG
  2. Set parameters (e.g. frequencies, power) of MW
  3. Configure pulse sequences for ODMR experiments
  4. Configure the counting module of Tagger
• Start devices & Acquire data
  1. run the "scheduler" (e.g. execute scheduler.run() method)
  2. the result will be saved into properties scheduler.result and scheduer.result_detail
• Analysis & Visualization
  1. visualize your result (e.g. contrast figure, counting figure corresponding to frequencies)
  2. use suitable analytical functions to fit your data curve

Code example

More examples could be found in the example folder. Herein is a typical scheduling pipeline instance.

# ------- Pulse ODMR Measurement -------
# configure parameters
channel_dict = {
    'laser': 1,
    'mw': 2,
    'apd': 3,
    'tagger': 5
}
tagger_input = {'apd': 1, 'asg': 2}

scheduler = PulseScheduler()  # Pulse-ODMR scheduler instance
scheduler.channel = channel_dict  # set ASG control channels
scheduler.tagger_input = tagger_input  # set tagger input channels
scheduler.configure_mw_paras(power=10)  # configure MW power
scheduler.configure_odmr_seq(t_init, t_mw, t_read_sig=400, t_read_ref=400, inter_init_mw=inter_init_mw, N=N)
scheduler.set_mw_freqs(freq_start, freq_end, freq_step)  # set measured frequencies
scheduler.configure_tagger_counting(reader='cbm')  # 'Counter Between Markers' measurement mode

# run
scheduler.run_scanning()
scheduler.save_result(fname)  # actually, this method is automatically called in the run_scanning() method

# result analysis
counts_sig_ref = scheduler.result  # [freqs, counts, counts_reference]
contrast = [sig / ref for sig, ref in
            zip(counts_sig_ref[0], counts_sig_ref[1])]  # calculate contrast (relative fluorescence intensity)

Addition

GUI software

A Graphical User Interface (GUI) version software based on this SDK is also implemented and has been used well in our QIM group. It is also published on the author's GitHub page.

Frequently Asked Questions

Q: Where should I get to know more about Odmactor?

A: If you have more demand or cooperation willingness, please contact to the Quantum Information and Materials Group of the University of Arizona.

Q: Can I modify this set of programs for my own research?

A: Of course. The Odmactor is a set of open-source programs for promising research and education. We are glad that more people can use or modify it. If you have more ideas or suggestions, welcome to contact to us!

Copyright and License

Odmactor uses the MIT license.