Cryogenic W-band Electron Spin Resonance Probehead with an Integral Cryogenic Low Noise Amplifier

TL;DR

This paper presents a cryogenic W-band ESR probehead with an integrated low-noise amplifier that significantly improves sensitivity by reducing signal loss and noise, enabling more precise detection of paramagnetic spins.

Contribution

The work introduces a novel cryogenic W-band ESR probehead with an integrated low-noise amplifier and microresonator, enhancing signal amplification and sensitivity at cryogenic temperatures.

Findings

Achieved spin sensitivity of ~3×10^5 spins/√Hz for phosphorus-doped silicon.

Demonstrated low-noise performance of the cryogenic LNA compared to room temperature amplifiers.

Potential to improve sensitivity to below 10^3 spins/√Hz with optimized sample geometry.

Abstract

The quest to enhance the sensitivity of electron spin resonance (ESR) is an ongoing challenge. One potential strategy involves increasing the frequency, for instance, moving from Q-band (approximately 35 GHz) to W-band (approximately 94 GHz). However, this shift typically results in higher transmission and switching losses, as well as increased noise in signal amplifiers. In this work, we address these shortcomings by employing a W-band probehead integrated with a cryogenic low-noise amplifier (LNA) and a microresonator. This configuration allows us to position the LNA close to the resonator, thereby amplifying the acquired ESR signal with minimal losses. Furthermore, when operated at cryogenic temperatures, the LNA exhibits unparalleled noise levels that are significantly lower than those of conventional room temperature LNAs. We detail the novel probehead design and provide some experimental results at room temperature as well as cryogenic temperatures for representative paramagnetic samples. We find, for example, that spin sensitivity of ~3*10^5 spins/sqrt(Hz) is achieved for a sample of phosphorus doped 28Si, even for sub-optimal sample geometry with potential improvement to <10^3 spins/sqrt(Hz) in more optimal scenarios.