A5013634299- Physics of Superconductivity and Magnetism
- Superconducting Materials and Applications
- Photonic and Optical Devices
- Quantum and electron transport phenomena
- CCD and CMOS Imaging Sensors
- Particle accelerators and beam dynamics
- Advanced Memory and Neural Computing
- Neural Networks and Reservoir Computing
- Gyrotron and Vacuum Electronics Research
- Atomic and Subatomic Physics Research
- Quantum many-body systems
- Advancements in Semiconductor Devices and Circuit Design
- Mechanical and Optical Resonators
- Advanced Optical Sensing Technologies
- Advanced Thermodynamics and Statistical Mechanics
- Magnetic confinement fusion research
- Particle Accelerators and Free-Electron Lasers
- Superconductivity in MgB2 and Alloys
- Advanced Semiconductor Detectors and Materials
- Advanced Fiber Laser Technologies
- Nanowire Synthesis and Applications
- Scientific Research and Discoveries
- Photoacoustic and Ultrasonic Imaging
- Stochastic Gradient Optimization Techniques
- Superconducting and THz Device Technology
University of Colorado Boulder
2023-2025
National Institute of Standards and Technology
2022-2024
University of Maryland, College Park
2019-2021
Although superconducting nanowire single-photon detectors (SNSPDs) are a promising technology for quantum optics, metrology, and astronomy, they currently lack readout architecture that is scalable to the megapixel regime beyond. In this work, we have designed demonstrated such an SNSPDs, called thermally-coupled imager (TCI). The TCI uses combination of time-of-flight delay lines thermal coupling create can scale large array sizes, allows neighboring operate independently, requires only...
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in field. It emphasizes critical role cross-disciplinary collaboration this rapidly evolving
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating loss function in hardware it via stochastic descent. We extend framework to include both weight node perturbation, discuss advantages disadvantages each approach. investigate time train networks using MGD as network size task complexity. Previous research has suggested that methods do not scale well large problems, since these...
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating loss function in hardware it via stochastic descent. We extend framework to include both weight node perturbation discuss advantages disadvantages each approach. investigate time train networks using MGD as network size task complexity. Previous research has suggested that methods do not scale well large problems since these...
We apply time-dependent Ginzburg Landau (TDGL) numerical simulations to study the finite frequency electrodynamics of superconductors subjected intense rf magnetic field. Much recent TDGL work has focused on spatially uniform external field and largely ignores Meissner state screening response superconductor. In this work, we solve TGDL equations for a non-uniform created by point dipole in vicinity semi-infinite A novel two-domain simulation is performed accurately capture effect...
The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge the field circuit design simulation-how accurately account for inherent stochastic nature certain devices. While conventional deterministic models have served as indispensable tools designers, they fall short when it comes capturing subtle yet critical variability exhibited by many components. In this paper, we present an innovative approach that transcends...
We present multiplexed gradient descent (MGD), a framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimization techniques for online training of hardware networks. demonstrate its ability on modern machine learning datasets, including CIFAR-10 and Fashion-MNIST, compare performance backpropagation. Assuming realistic timescales parameters, our results indicate that these can network emerging platforms orders magnitude faster than the...
Superconducting electronics are among the most promising alternatives to conventional CMOS technology, thanks ultra-fast speed and ultra-high energy efficiency of superconducting devices. Having a cryogenic control processor is also crucial requirement for scaling existing quantum computers up thousands qubits. Despite showing outstanding efficiency, Josephson junction-based circuits suffer from several challenges such as flux trapping leading limited scalability, difficulty in driving high...
The performance of Nb superconducting radio-frequency (SRF) cavities in particle accelerators is often limited by breakdown events below the intrinsic limiting surface fields Nb. Though excellent rf properties have been achieved, a detailed understanding causal links between treatment, defects, and ultimate lacking. This study uses magnetic writer probe from conventional hard-disk drive as near-field microwave microscope, to localized response SRF-grade samples. reveals nonlinear due...
We report on initial efforts in the integration of superconducting nanowire single-photon detectors (SNSPDs) with vertically aligned carbon nanotubes (VACNTs) goal creating a wideband detector. SNSPDs provide high detection efficiencies and low dark count rates, while VACNTs are excellent broadband optical absorbers. Combining these technologies could potentially enable development highly sensitive versatile sensors for variety applications, such as spectroscopy, communication, imaging light...
Boron-doped diamond granular thin films are known to exhibit superconductivity with an optimal critical temperature of Tc=7.2 K. Here, we report the measured in-plane complex surface impedance boron-doped in microwave frequency range using a resonant technique. Experimentally inductance values good agreement estimates obtained from normal state sheet resistance material. The magnetic penetration depth dependence is consistent that fully gapped s-wave superconductor. should find application...
For the last 50 years, superconducting detectors have offered exceptional sensitivity and speed for detecting faint electromagnetic signals in a wide range of applications. These operate at very low temperatures generate minimum excess noise, making them ideal testing non-local nature reality, investigating dark matter, mapping early universe, performing quantum computation communication. Despite their appealing properties, however, there are currently no large-scale cameras - even largest...
We present multiplexed gradient descent (MGD), a framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimization techniques for online training of hardware networks. demonstrate its ability on modern machine learning datasets, including CIFAR-10 and Fashion-MNIST, compare performance backpropagation. Assuming realistic timescales parameters, our results indicate that these can network emerging platforms orders magnitude faster than the...
Superconducting electronics are among the most promising alternatives to conventional CMOS technology thanks ultra-fast speed and ultra-high energy efficiency of superconducting devices. Having a cryogenic control processor is also crucial requirement for scaling existing quantum computers up thousands qubits. Despite showing outstanding efficiency, Josephson junction-based circuits suffer from several challenges such as flux trapping leading limited scalability, difficulty in driving high...
The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge the field circuit design simulation: how accurately account for inherent stochastic nature certain devices. While conventional deterministic models have served as indispensable tools designers, they fall short when it comes capture subtle yet critical variability exhibited by many components. In this paper, we present an innovative approach that transcends...
Superconducting nanowire single-photon detectors (SNSPDs) have demonstrated outstanding performance metrics over a wide range of wavelengths, from the ultraviolet to long-wave infrared (LWIR). System detection efficiencies approach 100% at specific wavelengths with very low dark count rates and timing jitter. Recent experiments such as time-resolved photoluminescence mid-IR emitting semiconductors integration SNSPDs ion traps will be described.