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Xavier Rottenberg

Publications and source records attributed to Xavier Rottenberg.

3 recordsLinked to original sources

Cryogenic piezoelectric effects in thin film strontium titanate devices

Next generation quantum technologies will need to rely on efficient transduction between electrical, optical, and mechanical quantum degrees of freedom to generate large-scale entanglement over large distances. The performance of such transducers is fundamentally limited by the cryogenic properties of the underlying materials. Here, we demonstrate that engineering strain in ferroelectric thin-film strontium titanate ($\mathrm{SrTiO_3}$) not only results in an exceptionally large Pockels coefficient, but also in a robust linear piezoelectric response at cryogenic temperatures, surpassing previous thin-film benchmarks. We measure piezoelectric tensor elements of $d_{15} = 151.8 \pm 1.5$ pm/V and $d_{33} = 54.8 \pm 4$ pm/V, and an effective photoelastic coefficient of $p_{\mathrm{eff}}$ = 0.56 at 5~K. Utilizing these enhanced properties, we demonstrate the first $\mathrm{SrTiO_3}$-on-oxide acousto-optic modulator with a voltage-length product ($V_{\pi}L$) of $0.874 \pm 0.084$ V cm, outperforming state-of-the-art unreleased modulators that typically feature a $V_{\pi}L$ of a few V cm. Our results establish thin-film $\mathrm{SrTiO_3}$ as a promising material system for integrated quantum photonics operating at cryogenic temperatures.

physics.optics

Parameter-Efficient Deep Learning for Ultrasound-Based Human-Machine Interfaces

Ultrasound (US) has emerged as a promising modality for Human-Machine Interfaces (HMIs), with recent research efforts exploring its potential for Hand Pose Estimation (HPE). A reliable solution to this problem could introduce interfaces with simultaneous support for up to 23 degrees of freedom encompassing all hand and wrist kinematics, thereby allowing far richer and more intuitive interaction strategies. Despite these promising results, a systematic comparison of models, input modalities and training strategies is missing from the literature. Moreover, there is only one publicly available dataset, namely the Ultrasound Adaptive Prosthetic Control (Ultra-Pro) dataset, enabling reproducible benchmarking and iterative model development. In this paper, we compare the performance of six different deep learning models, selected based on diverse criteria, on this benchmark. We demonstrate that, by using a step learning rate scheduler and the envelope of the RF signals as input modality, our 4-layer deep UDACNN surpasses XceptionTime's performance by $2.28$ percentage points while featuring $87.52\%$ fewer parameters. This result ($77.72\%$) constitutes an absolute improvement of $0.88\%$ from previously reported baselines. According to our findings, the appropriate combination of model, preprocessing and training algorithm is crucial for optimizing HMI performance.

cs.HC

Non-contact photoacoustic imaging with a silicon photonics-based Laser Doppler Vibrometer

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive and bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical applications. While other non-contact detection methods can be bulky, in this paper, we demonstrate compact and contactless detection of photoacoustic signals on silicone samples embedded with ink-filled channels, mimicking tissue with blood-carrying veins. A silicon photonics-based Laser Doppler Vibrometer (LDV) detects the acoustic waves excited by a compact pulsed laser diode. By scanning the LDV beam over the surface of the sample, 2D photoacoustic images were reconstructed of the sample. Photoacoustic signals with absorption coefficients within the physiological range were detected by this setup.

physics.app-ph