arXiv subjects

D. Brunner

Publications and source records attributed to D. Brunner.

15 recordsLinked to original sources

arXiv2024

DEGAS 2 model validation study: comparison of measured and modeled helium and deuterium line emission arising from an external gas puff on Alcator C-Mod

The ability to accurately model and predict neutral transport in the boundary plasma is important for tokamak operation. Nevertheless, validation of neutral transport models can be challenging due to the difficulty in measuring neutral particle distributions. Taking advantage of the localized neutral gas puff associated with the Gas Puff Imaging (GPI) diagnostic on the Alcator C-Mod, a validation study of the neutral transport code DEGAS 2 has been performed for helium and deuterium neutrals. Absolutely calibrated measurements of helium and hydrogen line emission are compared with simulated emission from DEGAS 2, accounting for the measured gas flow rates and employing a realistic geometry. Good agreement in peak brightness and profile shape is found for a deuterium puff case. However, helium line emission measurements are found to be lower by a factor of three than that predicted in the steady state DEGAS 2 simulations for a wide range in plasma density. Discrepancies in the light emission profile shape are evident as well. As possible causes for this discrepancy, two effects are discussed. First is the effect of local cooling due to gas puff. Second is the role of time-dependent turbulence effects on neutral penetration.

physics.plasm-ph↗

arXiv2021

Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing

Artificial neural networks have become a staple computing technique in many fields. Yet, they present fundamental differences with classical computing hardware in the way they process information. Photonic implementations of neural network architectures potentially offer fundamental advantages over their electronic counterparts in terms of speed, processing parallelism, scalability and energy efficiency. Scalable and high performance photonic neural networks (PNNs) have been demonstrated, yet they remain scarce. In this work, we study the performance of such a scalable, fully parallel and autonomous PNN based on a large area vertical-cavity surface-emitting laser (LA-VCSEL). We show how the performance varies with different physical parameters, namely, injection wavelength, injection power, and bias current. Furthermore, we link these physical parameters to the general computational measures of consistency and dimensionality. We present a general method of gauging dimensionality in high dimensional nonlinear systems subject to noise, which could be applied to many systems in the context of neuromorphic computing. Our work will inform future implementations of spatially multiplexed VCSEL PNNs.

cs.ET↗

arXiv2021

Photonic neuromorphic computing using vertical cavity semiconductor lasers

Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. In this review we provide an overview on vertical-cavity surface-emitting lasers (VCSELs) and how these high-performance electro-optical components either implement or are combined with additional photonic hardware to demonstrate points (i-iii). In the neurmorphic photonics' context, VCSELs are of exceptional interest as they are compatible with CMOS fabrication, readily achieve 30\% wall-plug efficiency and >30~GHz modulation bandwidth and hence are highly energy efficient and ultra-fast. Crucially, they react highly nonlinear to optical injection as well as to electrical modulation, making them highly suitable as all-optical as well as electro-optical photonic neurons. Their optical cavities are wavelength-limited, and standard semiconductor growth and lithography enables non-classical cavity configurations and geometries. This enables excitable VCSELs (i.e. spiking VCSELs) to finely control their temporal and spatial coherence, to unlock Terahertz bandwidths through spin-flip effects, and even to leverage cavity quantum electrodynamics to further boost their efficiency. Finally, as VCSEL arrays they are compatible with standard 2D photonic integration, but their emission vertical to the substrate makes them ideally suited for scalable integrated networks leveraging 3D photonic waveguides. Here, we discuss the implementation of spatially as well as temporally multiplexed VCSEL neural networks and reservoirs, computation on the basis of excitable VCSELs as photonic spiking neurons, as well as concepts and advances in the fabrication of VCSELs and microlasers.

cs.ET↗

arXiv2021

Tutorial: Photonic Neural Networks in Delay Systems

Photonic delay systems have revolutionized the hardware implementation of Recurrent Neural Networks and Reservoir Computing in particular. The fundamental principles of Reservoir Computing strongly benefit a realization in such complex analog systems. Especially delay systems, potentially providing large numbers of degrees of freedom even in simple architectures, can efficiently be exploited for information processing. The numerous demonstrations of their performance led to a revival of photonic Artificial Neural Network. Today, an astonishing variety of physical substrates, implementation techniques as well as network architectures based on this approach have been successfully employed. Important fundamental aspects of analog hardware Artificial Neural Networks have been investigated, and multiple high-performance applications have been demonstrated. Here, we introduce and explain the most relevant aspects of Artificial Neural Networks and delay systems, the seminal experimental demonstrations of Reservoir Computing in photonic delay systems, plus the most recent and advanced realizations.

cs.ET↗

arXiv2021

Optical pumping of quantum dot micropillar lasers

Arrays of quantum dot micropillar lasers are an attractive technology platform for various applications in the wider field of nanophotonics. Of particular interest is the potential efficiency enhancement as consequence of cavity quantum electrodynamics effects which makes them prime candidates for next generation photonic neurons in neural network hardware. However, in particular for optical pumping their power-conversion efficiency can be very low. Here we perform an in-depth experimental analysis of quantum dot microlasers and investigate their input-output relationship over a wide range of optical pumping conditions. We find that the current energy efficiency limitation is caused by disadvantageous optical pumping concepts and by a low exciton conversion efficiency. Our results indicate that for non-resonant pumping into the GaAs matrix (wetting layer), 3.4% (0.6%) of the optical pump is converted into lasing-relevant excitons, and of those only 2% (0.75%) provide gain to the lasing transition. Based on our findings we propose to improve the pumping efficiency by orders of magnitude by increasing the aluminium content of the AlGaAs/GaAs mirror pairs in the upper Bragg reflector.

physics.optics↗

arXiv2020

Developing of a photonic hardware platform for brain-inspired computing based on $5\times5$ VCSEL arrays

Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays of high-quality vertical cavity surface emitting lasers (VCSELs). The developed $5\times5$ VCSEL arrays provide high optical injection locking efficiency through homogeneous fabrication combined with individual control over the laser wavelengths. Injection locking is crucial for the reliable processing of information in VCSEL-based photonic neurons, and we demonstrate the suitability of the VCSEL arrays by injection locking measurements and current-induced spectral fine-tuning. We find that our investigated array can readily be tuned to the required spectral homogeneity, and as such show that VCSEL arrays based on our technology can act as highly energy efficient and ultra-fast photonic neurons for next generation photonic neural networks. Combined with fully parallel photonic networks our substrates are promising for ultra-fast operation reaching 10s of GHz bandwidths, and we show that a nonlinear transformation based on our lasers will consume only about 100 fJ per VCSEL, which is highly competitive, compared to other platforms.

cs.ET↗

arXiv2020

Comparison between mirror Langmuir probe and gas puff imaging measurements of intermittent fluctuations in the Alcator C-Mod scrape-off layer

Statistical properties of the scrape-off layer (SOL) plasma fluctuations are studied in ohmically heated plasmas in the Alcator C-Mod tokamak. For the first time, plasma fluctuations as well as parameters that describe the fluctuations are compared across measurements from a mirror Langmuir probe (MLP) and from gas-puff imaging (GPI) that sample the same plasma discharge. This comparison is complemented by an analysis of line emission time-series data, synthesized from the MLP electron density and temperature measurements. The fluctuations observed by the MLP and GPI typically display relative fluctuation amplitudes of order unity together with positively skewed and flattened probability density functions. Such data time series are well described by an established stochastic framework which model the data as a superposition of uncorrelated, two-sided exponential pulses. The most important parameter of the process is the intermittency parameter, {\gamma} = {\tau}d / {\tau}w where {\tau}d denotes the duration time of a single pulse and {\tau}w gives the average waiting time between consecutive pulses. Here we show, using a new deconvolution method, that these parameters can be consistently estimated from different statistics of the data. We also show that the statistical properties of the data sampled by the MLP and GPI diagnostic are very similar. Finally, a comparison of the GPI signal to the synthetic line-emission time series suggests that the measured emission intensity can not be explained solely by a simplified model which neglects neutral particle dynamics.

physics.plasm-ph↗

arXiv2018

Conceptual design study for heat exhaust management in the ARC fusion pilot plant

The ARC pilot plant conceptual design study has been extended beyond its initial scope [B. N. Sorbom et al., FED 100 (2015) 378] to explore options for managing ~525 MW of fusion power generated in a compact, high field (B_0 = 9.2 T) tokamak that is approximately the size of JET (R_0 = 3.3 m). Taking advantage of ARC's novel design - demountable high temperature superconductor toroidal field (TF) magnets, poloidal magnetic field coils located inside the TF, and vacuum vessel (VV) immersed in molten salt FLiBe blanket - this follow-on study has identified innovative and potentially robust power exhaust management solutions.

physics.ins-det↗

arXiv2018

Outlier classification using Autoencoders: application for fluctuation driven flows in fusion plasmas

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. This way, we avoid to define complicate threshold rules to identify outliers, which requires strong assumptions and introduce biases in the analysis. Instead, these rules are learned from the data by statistical inference By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat flux are between approximately 5 and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

physics.plasm-ph↗

arXiv2017

Effects of dynamic-demand-control appliances on the power grid frequency

Power grid frequency control is a demanding task requiring expensive idle power plants to adapt the supply to the fluctuating demand. An alternative approach is controlling the demand side in such a way that certain appliances modify their operation to adapt to the power availability. This is specially important to achieve a high penetration of renewable energy sources. A number of methods to manage the demand side have been proposed. In this work we focus on dynamic demand control (DDC), where smart appliances can delay their switchings depending on the frequency of the system. We introduce a simple model to study the effects of DDC on the frequency of the power grid. The model includes the power plant equations, a stochastic model for the demand that reproduces, adjusting a single parameter, the statistical properties of frequency fluctuations measured experimentally, and a generic DDC protocol. We find that DDC can reduce small and medium size fluctuations but it can also increase the probability of observing large frequency peaks due to the necessity of recovering pending task. We also conclude that a deployment of DDC around 30-40\% already allows a significant reduction of the fluctuations while keeping the number of pending tasks low.

physics.soc-ph↗

arXiv2015

Kinetic modeling of divertor heat load fluxes in the Alcator C-Mod and DIII-D tokamaks

The guiding-center kinetic neoclassical transport code, XGC0, [C.S. Chang et. al, Phys. Plasmas 11, 2649 (2004)] is used to compute the heat fluxes and the heat-load width in the outer divertor plates of Alcator C-Mod and DIII-D tokamaks. The dependence of the width of heat-load fluxes on neoclassical effects, neutral collisions and anomalous transport is investigated using the XGC0 code. The XGC0 code includes realistic X-point geometry, a neutral source model, the effects of collisions, and a diffusion model for anomalous transport. It is observed that width of the XGC0 neoclassical heat-load is approximately inversely proportional to the total plasma current $I_{\rm p}$. The scaling of the width of the divertor heat-load with plasma current is examined for an Alcator C-Mod discharge and four DIII-D discharges. The scaling of the divertor heat-load width with plasma current is found to be weaker in the Alcator C-Mod discharge compared to scaling found in the DIII-D discharges. The effect of neutral collisions on the $1/I_{\rm p}$ scaling of heat-load width is shown not to be significant. Although inclusion of poloidally uniform anomalous transport results in a deviation from the $1/I_{\rm p}$ scaling, the inclusion of the anomalous transport that is driven by ballooning-type instabilities results in recovering the neoclassical $1/I_{\rm p}$ scaling. The Bohm or Gyro-Bohm scalings of anomalous transport does not strongly affect the dependence of the heat-load width on plasma current. The inclusion of anomalous transport, in general, results in widening the width of neoclassical divertor heat-load and enhances the neoclassical heat-load fluxes on the divertor plates. Understanding heat transport in the tokamak scrape-off layer plasmas is important for strengthening the basis for predicting divertor conditions in ITER.

physics.plasm-ph↗

arXiv2011

Laser spectroscopy of individual quantum dots charged with a single hole

We characterize the positively charged exciton (X1+) in single InGaAs quantum dots using resonant laser spectroscopy. Three samples with different dopant species (Be or C as acceptors, Si as a donor) are compared. The p-doped samples exhibit larger inhomogeneous broadening (x3) and smaller absorption contrast (x10) than the n-doped sample. For X1+ in the Be-doped sample, a dot dependent non-linear Fano effect is observed, demonstrating coupling to degenerate continuum states. However, for the C-doped sample the X1+ lineshape and saturation broadening follows isolated atomic transition behaviour. This C-doped device structure is useful for single hole spin initialization, manipulation, and measurement.

cond-mat.mes-hall↗

arXiv2010

Controlling the Interaction of Electron and Nuclear Spins in a Tunnel-Coupled Quantum Dot

We present a technique for manipulating the nuclear spins and the emission polarization from a single optically active quantum dot. When the quantum dot is tunnel coupled to a Fermi sea, we have discovered a natural cycle in which an electron spin is repeatedly created with resonant optical excitation. The spontaneous emission polarization and the nuclear spin polarization exhibit a bistability. For a sigma(+) pump, the emission switches from sigma(+) to sigma(-) at a particular detuning of the laser. Simultaneously, the nuclear spin polarization switches from positive to negative. Away from the bistability, the nuclear spin polarization can be changed continuously from negative to positive, allowing precise control via the laser wavelength.

cond-mat.mes-hall↗

arXiv2008

Dressed excitonic states and quantum interference in a three-level quantum dot ladder system

We observe dressed states and quantum interference effects in a strongly driven three-level quantum dot ladder system. The effect of a strong coupling field on one dipole transition is measured by a weak probe field on the second dipole transition using differential reflection. When the coupling energy is much larger than both the homoge-neous and inhomogeneous linewidths an Autler-Townes splitting is observed. Striking differences are observed when the transitions resonant with the strong and weak fields are swapped, particularly when the coupling energy is nearly equal to the measured linewidth. This result is attributed to quantum interference: a modest destructive or constructive interference is observed depending on the pump / probe geometry. The data demonstrate that coher-ence of both the bi-exciton and the exciton is maintained in this solid-state system, even under intense illumina-tion, which is crucial for prospects in quantum information processing and non-linear optical devices.

cond-mat.mtrl-sci↗

arXiv2006

Single electron charging of impurity sites visualized by scanning gate experiments on a quantum point contact

A quantum point contact (QPC) patterned on a two-dimensional electron gas is investigated with a scanning gate setup operated at a temperature of 300 mK. The conductance of the point contact is recorded while the local potential is modified by scanning the tip. Single electron charging of impurities induced by the local potential is observed as a stepwise conductance change of the constriction. By selectively changing the state of some of these impurities, it is possible to observe changes in transmission resonances of the QPC. The location of such impurities is determined, and their density is estimated to be below 50 per μm^2, corresponding to less than 1 % of the doping concentration.

cond-mat.mes-hall↗