High-Density Covalent Grafting of Spin-Active Molecular Moieties to Diamond Surfaces.

Functionalization of diamond surfaces with TEMPO and other surface paramagnetic species represents one approach to the implementation of novel chemical detection schemes that make use of shallow quantum color defects such as silicon-vacancy (SiV) and nitrogen-vacancy (NV) centers. Yet, prior approaches to quantum-based chemical sensing have been hampered by the absence of high-quality surface functionalization schemes for linking radicals to diamond surfaces. Here, we demonstrate a highly controlled approach to the functionalization of diamond surfaces with carboxylic acid groups via all-carbon tethers of different lengths, followed by covalent chemistry to yield high-quality, TEMPO-modified surfaces. Our studies yield estimated surface densities of 4-amino-TEMPO of approximately 1.4 molecules nm-2 on nanodiamond (varying with molecular linker length) and 3.3 molecules nm-2 on planar diamond. These values are higher than those reported previously using other functionalization methods. The ζ-potential of nanodiamonds was used to track reaction progress and elucidate the regioselectivity of the reaction between ethenyl and carboxylate groups and surface radicals.

Quantum control and process tomography of a semiconductor quantum dot hybrid qubit.

The similarities between gated quantum dots and the transistors in modern microelectronics--in fabrication methods, physical structure and voltage scales for manipulation--have led to great interest in the development of quantum bits (qubits) in semiconductor quantum dots. Although quantum dot spin qubits have demonstrated long coherence times, their manipulation is often slower than desired for important future applications, such as factoring. Furthermore, scalability and manufacturability are enhanced when qubits are as simple as possible. Previous work has increased the speed of spin qubit rotations by making use of integrated micromagnets, dynamic pumping of nuclear spins or the addition of a third quantum dot. Here we demonstrate a qubit that is a hybrid of spin and charge. It is simple, requiring neither nuclear-state preparation nor micromagnets. Unlike previous double-dot qubits, the hybrid qubit enables fast rotations about two axes of the Bloch sphere. We demonstrate full control on the Bloch sphere with π-rotation times of less than 100 picoseconds in two orthogonal directions, which is more than an order of magnitude faster than any other double-dot qubit. The speed arises from the qubit's charge-like characteristics, and its spin-like features result in resistance to decoherence over a wide range of gate voltages. We achieve full process tomography in our electrically controlled semiconductor quantum dot qubit, extracting high fidelities of 85 per cent for X rotations (transitions between qubit states) and 94 per cent for Z rotations (phase accumulation between qubit states).

Gate fidelity and coherence of an electron spin in an Si/SiGe quantum dot with micromagnet.

The gate fidelity and the coherence time of a quantum bit (qubit) are important benchmarks for quantum computation. We construct a qubit using a single electron spin in an Si/SiGe quantum dot and control it electrically via an artificial spin-orbit field from a micromagnet. We measure an average single-qubit gate fidelity of ∼99% using randomized benchmarking, which is consistent with dephasing from the slowly evolving nuclear spins in the substrate. The coherence time measured using dynamical decoupling extends up to ∼400 µs for 128 decoupling pulses, with no sign of saturation. We find evidence that the coherence time is limited by noise in the 10-kHz to 1-MHz range, possibly because charge noise affects the spin via the micromagnet gradient. This work shows that an electron spin in an Si/SiGe quantum dot is a good candidate for quantum information processing as well as for a quantum memory, even without isotopic purification.

Embracing the quantum limit in silicon computing.

Quantum computers hold the promise of massive performance enhancements across a range of applications, from cryptography and databases to revolutionary scientific simulation tools. Such computers would make use of the same quantum mechanical phenomena that pose limitations on the continued shrinking of conventional information processing devices. Many of the key requirements for quantum computing differ markedly from those of conventional computers. However, silicon, which plays a central part in conventional information processing, has many properties that make it a superb platform around which to build a quantum computer.

Thickness-Dependent Cross-Plane Thermal Conductivity Measurements of Exfoliated Hexagonal Boron Nitride.

Submicrometer-thick layers of hexagonal boron nitride (hBN) exhibit high in-plane thermal conductivity and useful optical properties, and serve as dielectric encapsulation layers with low electrostatic inhomogeneity for graphene devices. Despite the promising applications of hBN as a heat spreader, the thickness dependence of its cross-plane thermal conductivity is not known, and the cross-plane phonon mean free paths (MFPs) have not been measured. We measure the cross-plane thermal conductivity of hBN flakes exfoliated from bulk crystals. We find that submicrometer thick flakes exhibit thermal conductivities up to 8.1 ± 0.5 W m-1 K-1 at 295 K, which exceeds previously reported bulk values by more than 60%. Surprisingly, the average phonon mean free path is found to be several hundred nanometers at room temperature, a factor of 5 larger than previous predictions. When planar twist interfaces are introduced into the crystal by mechanically stacking multiple thin flakes, the cross-plane thermal conductivity of the stack is found to be a factor of 7 below that of individual flakes with similar total thickness, thus providing strong evidence that phonon scattering at twist boundaries limits the maximum phonon MFPs. These results have important implications for hBN integration in nanoelectronics and improve our understanding of thermal transport in two-dimensional materials.

Electronic transport in nanometre-scale silicon-on-insulator membranes.

The widely used 'silicon-on-insulator' (SOI) system consists of a layer of single-crystalline silicon supported on a silicon dioxide substrate. When this silicon layer (the template layer) is very thin, the assumption that an effectively infinite number of atoms contributes to its physical properties no longer applies, and new electronic, mechanical and thermodynamic phenomena arise, distinct from those of bulk silicon. The development of unusual electronic properties with decreasing layer thickness is particularly important for silicon microelectronic devices, in which (001)-oriented SOI is often used. Here we show--using scanning tunnelling microscopy, electronic transport measurements, and theory--that electronic conduction in thin SOI(001) is determined not by bulk dopants but by the interaction of surface or interface electronic energy levels with the 'bulk' band structure of the thin silicon template layer. This interaction enables high-mobility carrier conduction in nanometre-scale SOI; conduction in even the thinnest membranes or layers of Si(001) is therefore possible, independent of any considerations of bulk doping, provided that the proper surface or interface states are available to enable the thermal excitation of 'bulk' carriers in the silicon layer.

Distance dependence of neuronal growth on nanopatterned gold surfaces.

Understanding network development in the brain is of tremendous fundamental importance, but it is immensely challenging because of the complexity of both its architecture and function. The mechanisms of axonal navigation to target regions and the specific interactions with guidance factors such as membrane-bound proteins, chemical gradients, mechanical guidance cues, etc., are largely unknown. A current limitation for the study of neural network formation is the ability to control precisely the connectivity of small groups of neurons. A first step in designing such networks is to understand the "rules" central nervous system (CNS) neurons use to form functional connections with one another. Here we begin to delineate novel rules for growth and connectivity of small numbers of neurons patterned on Au substrates in simplified geometries. These studies yield new insights into the mechanisms determining the organizational features present in intact systems. We use a previously reported atomic force microscopy (AFM) nanolithography method to control precisely the location and growth of neurons on these surfaces. By examining a series of systems with different geometrical parameters, we quantitatively and systematically analyze how neuronal growth depends on these parameters.

Ray-based framework for state identification in quantum dot devices.

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the "ray-based classification (RBC) framework," we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82% accuracy benchmark from the experimental implementation of image-based classification techniques from prior work, while reducing the number of measurement points needed by up to 70%. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs. This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.

Predicting the results of chemical vapor deposition growth of suspended carbon nanotubes.

The successful growth of suspended carbon nanotubes is normally based on purely empirical results. Here we demonstrate the ability to predict the successful suspension of nanotubes across a range of trench widths by combining experimental growth data with a theoretical description of nanotube mechanics at the growth temperature. We show that rare thermal oscillations much larger than the rms amplitude combined with the large nanotube-substrate adhesion energy together are responsible for unsuccessful nanotube suspensions. We derive an upper limit on the number of deleterious nanotube-substrate interactions that can be tolerated before successful growth becomes impossible, and we are able to accurately explain literature reports of suspended nanotube growth. The methodology developed here should enable improved growth yields of suspended nanotubes, and it provides a framework in which to analyze the role of nanotube-substrate interactions during nanotube growth by chemical vapor deposition.

Autotuning of double dot devices in situ with machine learning.

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

Dielectrophoretic manipulation and real-time electrical detection of single-nanowire bridges in aqueous saline solutions.

Dielectrophoretic manipulation of nanoscale materials is typically performed in nonionic, highly insulating solvents. However, biomolecular recognition processes, such as DNA hybridization and protein binding, typically operate in highly conducting, aqueous saline solutions. Here, we report investigations of the manipulation and real-time detection of individual nanowires bridging microelectrode gaps in saline solutions. Measurements of the electrode impedance versus frequency show a crossover in behavior at a critical frequency that is dependent on the ionic strength. We demonstrate that by operating above this critical frequency, it is possible to use dielectrophoresis to manipulate nanowires across electrode gaps in saline solutions. By using electrical ground planes and nulling schemes to reduce the background currents, we further demonstrate the ability to electrically detect bridging and unbridging events of individual nanowires in saline solutions. The ability to both manipulate and detect bridging events with electrical signals provides a pathway toward automated assembly of nanoscale devices that incorporate biomolecular recognition elements.

Silicon Nanomembranes with Hybrid Crystal Orientations and Strain States.

Methods to integrate different crystal orientations, strain states, and compositions of semiconductors in planar and preferably flexible configurations may enable nontraditional sensing-, stimulating-, or communication-device applications. We combine crystalline-silicon nanomembranes, patterning, membrane transfer, and epitaxial growth to demonstrate planar arrays of different orientations and strain states of Si in a single membrane, which is then readily transferable to other substrates, including flexible supports. As examples, regions of Si(001) and Si(110) or strained Si(110) are combined to form a multicomponent, single substrate with high-quality narrow interfaces. We perform extensive structural characterization of all interfaces and measure charge-carrier mobilities in different regions of a 2D quilt. The method is readily extendable to include varying compositions or different classes of materials.

Critical oxide thickness for efficient single-walled carbon nanotube growth on silicon using thin SiO2 diffusion barriers.

The ability to integrate carbon nanotubes, especially single-walled carbon nanotubes, seamlessly onto silicon would expand their range of applications considerably. Though direct integration using chemical vapor deposition is the simplest method, the growth of single-walled carbon nanotubes on bare silicon and on ultrathin oxides is greatly inhibited due to the formation of a noncatalytic silicide. Using X-ray photoelectron spectroscopy, we show that silicide formation occurs on ultrathin oxides due to thermally activated metal diffusion through the oxide. Silicides affect the growth of single-walled nanotubes more than multi-walled nanotubes due to the increased kinetics at the higher single-walled nanotube growth temperature. We demonstrate that nickel and iron catalysts, when deposited on clean silicon or ultrathin silicon dioxide layers, begin to form silicides at relatively low temperatures, and that by 900 degrees C, all of the catalyst has been incorporated into the silicide, rendering it inactive for subsequent single-walled nanotube growth. We further show that a 4-nm silicon dioxide layer is the minimum diffusion barrier thickness that allows for efficient single-walled nanotube growth.

Electronic Transport Properties of Epitaxial Si/SiGe Heterostructures Grown on Single-Crystal SiGe Nanomembranes.

To assess possible improvements in the electronic performance of two-dimensional electron gases (2DEGs) in silicon, SiGe/Si/SiGe heterostructures are grown on fully elastically relaxed single-crystal SiGe nanomembranes produced through a strain engineering approach. This procedure eliminates the formation of dislocations in the heterostructure. Top-gated Hall bar devices are fabricated to enable magnetoresistivity and Hall effect measurements. Both Shubnikov-de Haas oscillations and the quantum Hall effect are observed at low temperatures, demonstrating the formation of high-quality 2DEGs. Values of charge carrier mobility as a function of carrier density extracted from these measurements are at least as high or higher than those obtained from companion measurements made on heterostructures grown on conventional strain graded substrates. In all samples, impurity scattering appears to limit the mobility.

Exceptional charge transport properties of graphene on germanium.

The excellent charge transport properties of graphene suggest a wide range of application in analog electronics. While most practical devices will require that graphene be bonded to a substrate, such bonding generally degrades these transport properties. In contrast, when graphene is transferred to Ge(001) its conductivity is extremely high and the charge carrier mobility derived from the relevant transport measurements is, under some circumstances, higher than that of freestanding, edge-supported graphene. We measure a mobility of ∼ 5 × 10(5) cm(2) V(-1) s(-1) at 20 K, and ∼ 10(3) cm(2) V(-1) s(-1) at 300 K. These values are close to the theoretical limit for doped graphene. Carrier densities in the graphene are as high as 10(14) cm(-2) at 300 K.

Probing the electronic structure at semiconductor surfaces using charge transport in nanomembranes.

The electrical properties of nanostructures are extremely sensitive to their surface condition. In very thin two-dimensional crystalline-semiconductor sheets, termed nanomembranes, the influence of the bulk is diminished, and the electrical conductance becomes exquisitely responsive to the structure of the surface and the type and density of defects there. Its understanding therefore requires a precise knowledge of the surface condition. Here we report measurements, using nanomembranes, that demonstrate direct charge transport through the π* band of the clean reconstructed Si(001) surface. We determine the charge carrier mobility in this band. These measurements, performed in ultra-high vacuum to create a truly clean surface, lay the foundation for a quantitative understanding of the role of extended or localized surface states, created by surface structure, defects or adsorbed atoms/molecules, in modifying charge transport through semiconductor nanostructures.

Functionalization of single-wall carbon nanotubes with chromophores of opposite internal dipole orientation.

We report the functionalization of carbon nanotubes with two azobenzene-based chromophores with large internal dipole moments and opposite dipole orientations. The molecules are attached to the nanotubes noncovalently via a pyrene tether. A combination of characterization techniques shows uniform molecular coverage on the nanotubes, with minimal aggregation of excess chromophores on the substrate. The large on/off ratios and the subthreshold swings of the nanotube-based field-effect transistors (FETs) are preserved after functionalization, and different shifts in threshold voltage are observed for each chromophore. Ab initio calculations verify the properties of the synthesized chromophores and indicate very small charge transfer, confirming a strong, noncovalent functionalization.

Spectroscopic properties of nanotube-chromophore hybrids.

Recently, individual single-walled carbon nanotubes (SWNTs) functionalized with azo-benzene chromophores were shown to form a new class of hybrid nanomaterials for optoelectronics applications. Here we use a number of experimental and computational techniques to understand the binding, orientation, and nature of coupling between chromophores and the nanotubes, all of which are relevant to future optimization of these hybrid materials. We find that the binding energy between chromophores and nanotubes depends strongly on the type of tether that is used to bind the chromophores to the nanotubes. The pyrene tethers form a much stronger attachment to nanotubes compared to anthracene or benzene rings, resulting in more than 80% retention of bound chromophores post-processing. Density functional theory (DFT) calculations show that the binding energy of the chromophores to the nanotubes is maximized for chromophores parallel to the nanotube sidewall, even with the use of tethers; optical second harmonic generation measurements show that there is nonetheless a partial radial orientation of the chromophores on the nanotubes. We find weak electronic coupling between the chromophores and the SWNTs, consistent with noncovalent binding. This weak coupling is still sufficient to quench the chromophore fluorescence through a combination of static and dynamic processes. Photoluminescence measurements show a lack of significant energy transfer from the chromophores to isolated semiconducting nanotubes.

Positioning and guidance of neurons on gold surfaces by directed assembly of proteins using Atomic Force Microscopy.

We demonstrate that Atomic Force Microscopy nanolithography can be used to control effectively the adhesion, growth and interconnectivity of cortical neurons on Au surfaces. We demonstrate immobilization of neurons at well-defined locations on Au surfaces using two different types of patterned proteins: 1) poly-d-lysine (PDL), a positively charged polypeptide used extensively in tissue culture and 2) laminin, a component of the extracellular matrix. Our results show that both PDL and laminin patterns can be used to confine neuronal cells and to control their growth and interconnectivity on Au surfaces, a significant step towards the engineering of artificial neuronal assemblies with well-controlled neuron position and connections.

Influence of surface chemical modification on charge transport properties in ultrathin silicon membranes.

Ultrathin silicon-on-insulator, composed of a crystalline sheet of silicon bounded by native oxide and a buried oxide layer, is extremely resistive because of charge trapping at the interfaces between the sheet of silicon and the oxide. After chemical modification of the top surface with hydrofluoric acid (HF), the sheet resistance drops to values below what is expected based on bulk doping alone. We explain this behavior in terms of surface-induced band structure changes combined with the effective isolation from bulk properties created by crystal thinness.