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Automatic Tuning and Matching for NMR Probes Based on Physics-Informed Conditional Neural Processes
Zhida Zhai1, Zhenggang Li1,2, Ying He1
1Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a new few-shot learning method for automated tuning and matching (ATM) in Nuclear Magnetic Resonance (NMR) systems. It significantly reduces the number of measurements needed for accurate and rapid NMR signal acquisition.
Area of Science:
- Nuclear Magnetic Resonance (NMR) Spectroscopy
- Machine Learning in Scientific Instrumentation
- RF Engineering and Circuit Design
Background:
- NMR resonator tuning and matching are critical for high-sensitivity signal acquisition.
- Current automated tuning and matching (ATM) methods rely on slow iterative searches.
- In situ NMR detection demands rapid, real-time ATM with a wide dynamic range, challenging conventional approaches.
Purpose of the Study:
- To develop a physics-informed few-shot learning method for rapid and accurate NMR ATM.
- To address the limitations of iterative search strategies in conventional NMR ATM.
- To enable real-time, wideband, and multi-resonance-frequency ATM for demanding NMR applications.
Main Methods:
- Formulated tuning-and-matching as a structure and frequency-conditioned function regression task.
- Employed a conditional neural process (CNP) to learn cross-task priors from minimal real-machine measurements.
- Integrated a physics regularizer based on local input impedance sensitivity to penalize errors, especially under high-Q narrowband conditions.
Main Results:
- Demonstrated consistent improvements in tuning and matching accuracy and reduced sample requirements across multiple circuit topologies and frequencies.
- Achieved satisfactory performance with as few as 20 on-hardware collected samples.
- Showcased an attractive accuracy-cost tradeoff in cross-topology and cross-frequency scenarios.
Conclusions:
- The proposed physics-informed few-shot learning method offers a significant advancement in NMR ATM.
- It enables rapid, accurate, and efficient tuning and matching with minimal data requirements.
- The method holds strong potential for few-shot, rapid, real-time NMR detection and analysis.
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