Growth and Characterization of Type-II Submonolayer ZnCdTe/ZnCdSe Quantum Dot Superlattices for Efficient Intermediate Band Solar Cells

In this thesis, we discuss the growth procedure and the characterization results obtained for epitaxially grown submonolayer type-II quantum dot superlattices made of II-VI semiconductors. The goal behind this study is to show the feasibility of this novel material system in fabricating an efficient intermediate band solar cell. Intermediate band solar cells can potentially have an efficiency of 63.2% under full solar concentration, but the material systems investigated until now are far from optimum and are fraught with growth related issues including low quantum dot densities, presence of wetting layers, strain driven dislocations etc. Here, we have investigated a novel material system grown via migration enhanced epitaxy with stacked type-II ZnCdTe submonolayer quantum dots embedded in ZnCdSe matrix and having close to the optimal material parameters required for an IB material. Upon optimizing growth conditions for ZnTe/ZnSe multilayer quantum dot systems, the growth parameters were modified so as to obtain various ZnCdTe/ZnCdSe samples grown on InP substrates. An extensive characterization has been performed to investigate structural, optical as well as electrical properties of these multilayered structures. Finally, a preliminary device fabrication has been performed, which will provide definite guidelines towards optimization of an actual intermediate band solar cell structure. To restate, the objective of this thesis is to demonstrate successful growth and characterization of multilayer structures with embedded submonolayer type-II quantum dots in order to explore the possibility of employing them as an intermediate band material, with the goal of engineering an ultra-efficient intermediate band solar cell

Using deep learning to understand and mitigate the qubit noise environment

Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure

Spin readout via spin-to-charge conversion in bulk diamond nitrogen-vacancy ensembles

We demonstrate optical readout of ensembles of nitrogen-vacancy(NV) center spins in a bulk diamond sample via spin-to-charge conversion. A high power 594 nm laser is utilized to selectively ionize these paramagnetic defects in the spin state with a contrast of up to 12%. In comparison with the conventional 520 nm spin readout, spin-to-charge-conversion-based readout provides higher signal-to-noise ratio, with tenfold sensing measurement speedup for millisecond long pulse sequences. This level of performance was achieved for an NV- ionization of only 25%, limited by the ionization and readout laser powers. These observations pave the way to a range of high-sensitivity metrology applications where the use of NV- ensembles in bulk diamond has proven useful, including sensing and imaging of target materials overlaid on the diamond surface

Long-term data storage in diamond

The negatively charged nitrogen vacancy (NV−) center in diamond is the focus of widespread attention for applications ranging from quantum information processing to nanoscale metrology. Althoughmostwork so far has focused on the NV− optical and spin properties, control of the charge state promises complementary opportunities. One intriguing possibility is the long-term storage of information, a notion we hereby introduce using NV-rich, type 1b diamond. As a proof of principle, we use multicolor optical microscopy to read, write, and reset arbitrary data sets with twodimensional (2D) binary bit density comparable to present digital-video-disk (DVD) technology. Leveraging on the singular dynamics of NV− ionization, we encode information on different planes of the diamond crystal with no crosstalk, hence extending the storage capacity to three dimensions. Furthermore, we correlate the center’s charge state and the nuclear spin polarization of the nitrogen host and showthat the latter is robust to a cycle of NV− ionization and recharge. In combination with super-resolution microscopy techniques, these observations provide a route toward subdiffraction NV charge control, a regime where the storage capacity could exceed present technologies

On-Demand Generation of Neutral and Negatively-Charged Silicon-Vacancy Centers in Diamond

Point defects in wide-bandgap semiconductors are emerging as versatile resources for nanoscale sensing and quantum information science but our understanding of the photo-ionization dynamics is presently incomplete. Here we use two-color confocal microscopy to investigate the dynamics of charge in Type 1b diamond hosting nitrogen-vacancy (NV) and silicon-vacancy (SiV) centers. By examining the non-local fluorescence patterns emerging from local laser excitation, we show that in the simultaneous presence of photo-generated electrons and holes, SiV (NV) centers selectively transform into the negative (neutral) charge state. Unlike NVs, 532 nm illumination ionizes SiV- via a single photon process thus hinting at a comparatively shallower ground state. In particular, slower ionization rates at longer wavelengths suggest the latter lies approximately ~1.9 eV below the conduction band minimum. Building on the above observations we demonstrate on-demand SiV and NV charge initialization over large areas via green laser illumination of variable intensity

Long-term data storage in diamond

The negatively charged nitrogen vacancy (NV−) center in diamond is the focus of widespread attention for applications ranging from quantum information processing to nanoscale metrology. Althoughmostwork so far has focused on the NV− optical and spin properties, control of the charge state promises complementary opportunities. One intriguing possibility is the long-term storage of information, a notion we hereby introduce using NV-rich, type 1b diamond. As a proof of principle, we use multicolor optical microscopy to read, write, and reset arbitrary data sets with twodimensional (2D) binary bit density comparable to present digital-video-disk (DVD) technology. Leveraging on the singular dynamics of NV− ionization, we encode information on different planes of the diamond crystal with no crosstalk, hence extending the storage capacity to three dimensions. Furthermore, we correlate the center’s charge state and the nuclear spin polarization of the nitrogen host and showthat the latter is robust to a cycle of NV− ionization and recharge. In combination with super-resolution microscopy techniques, these observations provide a route toward subdiffraction NV charge control, a regime where the storage capacity could exceed present technologies

Vector detection of AC magnetic fields by Nitrogen-Vacancy centers of single orientation in diamond

Nitrogen-Vacancy (NV) centers in diamond have useful properties for detecting both AC and DC magnetic fields with high sensitivity at nano-scale resolution. Vector detection of AC magnetic fields can be achieved by using NV centers having three different orientations. Here, we propose a method to achieve this by using NV centers of single orientation. In this method, a static magnetic field is applied perpendicular to the NV axis, leading to strong mixing of the $m_{s}=-1$ and $1$ electron spin states. As a result, all three electron spin transitions of the triplet ground state have non-zero dipole moments, with each transition coupling to a single component of the magnetic field. This can be used to measure both strength and orientation of the applied AC field. To validate the technique, we perform a proof of principle experiment using a subset of ensemble NV centers in diamond, all having the same orientation.Comment: 7 pages, 5 figure

Optical patterning of trapped charge in nitrogen-doped diamond

The nitrogen-vacancy (NV) centre in diamond is emerging as a promising platform for solid-state quantum information processing and nanoscale metrology. Of interest in these applications is the manipulation of the NV charge, which can be attained by optical excitation. Here we use two-color optical microscopy to investigate the dynamics of NV photo-ionization, charge diffusion, and trapping in type-1b diamond. We combine fixed-point laser excitation and scanning fluorescence imaging to locally alter the concentration of negatively charged NVs, and to subsequently probe the corresponding redistribution of charge. We uncover the formation of spatial patterns of trapped charge, which we qualitatively reproduce via a model of the interplay between photo-excited carriers and atomic defects. Further, by using the NV as a probe, we map the relative fraction of positively charged nitrogen upon localized optical excitation. These observations may prove important to transporting quantum information between NVs or to developing three-dimensional, charge-based memories

Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise

Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately, the traditional approach to characterizing the environmental magnetic field fluctuations from the measured probe signal typically requires the experimenter's input, thus complicating the implementation of automated imaging protocols based on spectrally resolved noise. Here, we probe the response of color centers in diamond in the presence of externally engineered random magnetic signals, and implement a deep neural network to methodically extract information on their associated spectral densities. Building on a long sequence of successive measurements under different types of stimuli, we show that our network manages to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field with good fidelity under a broad set of conditions and with only a minimal measured data set, even in the presence of substantial experimental noise. These proof-of-principle results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging