⚛️ quantum physics

Optimizing Oscilloscope based Acquisition for Pulsed Optically Detected Magnetic Resonance Measurements

This paper presents an optimized framework using a digital oscilloscope with on-board averaging and analog filtering to efficiently acquire high-quality pulsed optically detected magnetic resonance (ODMR) data from nitrogen vacancy (NV) center ensembles, demonstrating improved signal visualization, noise suppression, and aliasing reduction for future instrument design.

Original authors: Anuvab Nandi, Sayan Chakraborty, Yashvardhan Jain, Kanav Sharma, Samiran Chakraborti, Himadri Himani, Abir Mondal, Sumit Mukherjee, Chiranjib Mitra

Published 2026-07-03

📖 5 min read🧠 Deep dive

CC0 1.0

Original authors: Anuvab Nandi, Sayan Chakraborty, Yashvardhan Jain, Kanav Sharma, Samiran Chakraborti, Himadri Himani, Abir Mondal, Sumit Mukherjee, Chiranjib Mitra

Original paper dedicated to the public domain under CC0 1.0 (http://creativecommons.org/publicdomain/zero/1.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to listen to a very faint, specific whisper (a magnetic signal) coming from a crowd of thousands of tiny, glowing fireflies (nitrogen vacancy centers in a diamond). The problem is that the room is noisy, the fireflies flicker unpredictably, and the microphone you are using is picking up a lot of static.

This paper is a guide on how to build a better "listening station" using a standard piece of lab equipment called a digital oscilloscope to hear that whisper clearly. Here is the breakdown of their method using simple analogies:

1. The Goal: Listening to the Fireflies

The researchers are studying "NV centers" in diamonds. Think of these as tiny, glowing sensors that change their brightness slightly when you hit them with microwaves and magnetic fields.

The Challenge: Usually, scientists use expensive, specialized gear to listen to just one firefly. But here, they are listening to a whole crowd (an ensemble). While this makes the signal louder, it also introduces new types of noise and requires a different way of listening.
The Tool: Instead of a custom-built machine, they used a digital oscilloscope. Think of this as a high-tech camera for electricity. It doesn't just take a picture; it records a movie of how the voltage changes over time. The authors argue this is a "Swiss Army Knife" for scientists because it helps them debug the setup, visualize the signal in real-time, and collect data all in one device.

2. The Problem: The "Static" in the Room

When they recorded the signal, it was messy. The paper identifies two main types of noise (static):

The "Buzz" (High-Frequency Noise): Like the hiss of a radio tuned between stations. This happens within a single recording.
The "Drift" (Low-Frequency Noise): Like the slow, creeping change in room temperature or a shaky hand. This happens between recordings. One recording might be slightly higher or lower than the next, not because of the signal, but because the equipment is drifting.

3. The Solution: The "Digital Filter" (Cleaning the Signal)

The researchers didn't just record the data; they processed it using a clever two-step math trick that acts like a noise-canceling headphone.

Step 1: The "Smoothie" (Averaging):
First, they took a chunk of the signal and averaged it out. Imagine taking a jagged, noisy line and smoothing it into a gentle curve. This gets rid of the high-frequency "buzz."
- Analogy: If you ask 100 people to shout a number and you take the average, the random shouting (noise) cancels out, leaving you with the true number.
Step 2: The "Subtraction" (Differencing):
Next, they looked at two specific parts of the signal: one before the microwave pulse (the reference) and one after (the signal). They subtracted the "before" from the "after."
- Analogy: Imagine you are trying to hear a friend speak in a noisy cafe. You remember how loud the cafe was before they spoke. When they speak, you mentally subtract that background noise. Since the "drift" (the slow changes) affects both the "before" and "after" equally, subtracting them cancels out the drift completely.

The Result: By combining these two steps, they created a "Band-Pass Filter." It blocks the high-pitched buzz and the low-pitched drift, letting only the "sweet spot" of the signal through.

4. Tuning the Radio: Finding the Sweet Spot

The paper also figured out how long they should listen for each signal (called "readout duration").

Too Short: You miss some of the signal, and the noise is still too loud.
Too Long: You start picking up too much of the "drift" again, and the signal gets weaker relative to the noise.
The Sweet Spot: They found a "Goldilocks" duration (about 200 microseconds) where the signal is strong, and the noise is minimized.

5. The "Stacking" Trick (Waveform Averaging)

Finally, they used the oscilloscope's built-in ability to stack multiple recordings on top of each other.

Analogy: Imagine taking a photo of a faint star. One photo is grainy. If you take 32 photos and stack them perfectly on top of each other, the random grain (noise) blurs out, but the star (the signal) gets brighter and clearer.
They showed that by stacking 32 waveforms, the noise dropped significantly, making the data much cleaner without needing more expensive hardware.

6. The "Anti-Aliasing" Filter

They also added a simple analog filter (a low-pass filter) to the hardware path.

Analogy: This is like putting a screen door on a window. It stops the "aliens" (high-frequency noise that the camera can't see properly) from tricking the camera into thinking they are something else (a phenomenon called aliasing). This ensured the digital data wasn't lying to them.

Summary

The paper claims that by using a standard digital oscilloscope and applying a specific math-based "cleaning" process (smoothing, subtracting, and stacking), they can get high-quality data from a crowd of diamond sensors. They proved that this method:

Visualizes the signal better than older methods.
Cancels out both fast noise and slow drifts.
Optimizes the timing to get the clearest picture.
Saves money by using one versatile tool instead of many expensive, specialized ones.

They did not claim this works for medical diagnoses or future quantum computers in this specific text; they strictly focused on proving that this specific setup works better for measuring these diamond sensors.

Technical Summary: Optimizing Oscilloscope-based Acquisition for Pulsed ODMR Measurements

Problem Statement
Nitrogen vacancy (NV) centers in diamond are a leading platform for quantum sensing and information processing. While single NV centers offer nanoscale spatial resolution, ensemble-based approaches provide orders of magnitude higher sensitivity with simpler, more cost-effective instrumentation. However, acquiring high-quality pulsed optically detected magnetic resonance (ODMR) data from ensembles requires robust detection schemes that can handle noise and optimize acquisition speed. Traditional setups often rely on complex, dedicated hardware. This work addresses the need for an efficient, intuitive, and cost-effective detection method using standard digital oscilloscopes, which are typically used for debugging but can also serve as primary data acquisition tools.

Methodology
The authors developed a framework for acquiring and processing pulsed ODMR data from NV ensembles using a digital oscilloscope. The experimental setup involves a 532 nm laser for spin initialization and readout, and microwave pulses for spin manipulation. The photoluminescence (PL) signal is detected by an avalanche photodetector (APD) and digitized by the oscilloscope.

The core of the methodology involves:

Signal Acquisition: Capturing raw waveforms containing laser and microwave pulse sequences. The study compared three configurations: direct oscilloscope input at 100 MHz and 20 MHz bandwidths, and input through a 1 MHz unity-gain preamplifier (acting as an analog low-pass filter).
Data Processing: The raw data is processed to extract the ODMR spectrum. This involves selecting reference and signal segments of duration $T_{ro}$ , calculating the mean intensity for each ( $I_{ref}$ and $I_{sig}$ ), and determining the contrast ( $D = I_{sig} - I_{ref}$ or relative contrast $C = D/I_{ref}$ ).
Noise Characterization: The authors analyzed noise in both the time and frequency domains. They distinguished between intra-waveform noise (fluctuations within a single trace) and inter-waveform noise (quasi-static drifts between traces), utilizing standard deviation and Allan deviation, respectively. Power spectral density (PSD) analysis was used to identify noise sources, including aliasing effects.
Filtering Analysis: The data processing steps were modeled as a digital filtering operation. The averaging of segments acts as a low-pass filter, while the subtraction of signal and reference acts as a high-pass (or low-reject) filter. The combination results in an effective band-pass filter.
Optimization: The study systematically tuned the readout duration ( $T_{ro}$ ) and the number of waveform averages ( $N_{avg}$ ) to maximize the Contrast-to-Noise Ratio (CNR).

Key Contributions and Results

Oscilloscope as a Primary Acquisition Tool: The paper demonstrates that a digital oscilloscope is not only suitable for debugging but can acquire high-quality ODMR data efficiently. Its on-board waveform averaging capability is shown to significantly improve measurement efficiency.
Noise Suppression via Digital Processing: The authors derived the transfer function of the data processing algorithm, showing it acts as a band-pass filter. This effectively suppresses a broad range of noise spectral components, particularly the quasi-static inter-waveform noise (drift) which is a major source of error in raw data. The processed data noise is shown to be dominated by white noise in the intermediate frequency band.
Role of Analog Filtering: The introduction of an analog low-pass filter (via a preamplifier) in the signal path was shown to improve measurements by removing aliasing, a critical issue when the sampling rate is finite.
Optimization of Parameters:
- Readout Duration ( $T_{ro}$ ): Increasing $T_{ro}$ reduces noise (scaling as $1/\sqrt{T_{ro}}$ ) but also reduces the absolute contrast. The CNR increases with $T_{ro}$ up to a saturation point (~260 $\mu$ s), after which further increases yield diminishing returns. A value of 200 $\mu$ s was identified as a suitable compromise.
- Waveform Averaging: On-board averaging reduces the noise in the raw data and the final processed data by a factor of $1/\sqrt{N_{avg}}$ , directly improving the CNR linearly with the square root of the number of averages.
Quantitative Metrics: The study establishes the Contrast-to-Noise Ratio (CNR) as a robust figure of merit for evaluating signal quality in pulsed ODMR, showing that relative contrast ( $C$ ) and absolute contrast ( $D$ ) yield equivalent CNRs in this context.

Significance and Claims
The authors claim that the framework developed provides a practical, low-cost alternative to dedicated DAQ systems for ensemble-based ODMR. By leveraging the oscilloscope's built-in features (averaging, real-time visualization) and optimizing the signal processing chain, the system achieves high-quality data acquisition with simplified debugging capabilities.

The paper asserts that the insights regarding noise spectral components, the effectiveness of the band-pass filtering inherent in the subtraction method, and the optimization of readout times and averaging can be extended to other detection techniques and material platforms. The work aims to guide the design of future dedicated instruments for ODMR by highlighting the critical roles of analog filtering (to prevent aliasing) and digital processing (to suppress specific noise bands). The authors remain modest, noting that while the system performs reliably even under conditions where other DAQ systems might struggle (e.g., high repolarization rates), the primary contribution is the demonstration of an optimized, accessible acquisition framework rather than a novel physical discovery.