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This article introduces a groundbreaking analytical workflow designed for the holistic characterisation, modelling and physical simulation of device heterostructures. Our innovative workflow autonomously, comprehensively and locally characterises the crystallographic information and 3D orientation of the crystal phases, the elemental composition, and the strain maps of devices from (scanning) transmission electron microscopy data. It converts a manual characterisation process that traditionally takes days into an automatic routine completed in minutes. This is achieved through a physics-guided artificial intelligence model that combines unsupervised and supervised machine learning in a modular way to provide a representative 3D description of the devices, materials structures, or samples under analysis. To culminate the process, we integrate the extracted knowledge to automate the generation of both 3D finite element and atomic models of millions of atoms acting as digital twins, enabling simulations that yield essential physical and chemical insights crucial for understanding the device's behaviour in practical applications. We prove this end-to-end workflow with a state-of-the-art materials platform based on SiGe planar heterostructures for hosting coherent and scalable spin qubits. Our workflow connects representative digital twins of the experimental devices with their theoretical properties to reveal the true impact that every atom in the structure has on their electronic properties, and eventually, into their functional quantum performance. Notably, the versatility of our workflow is demonstrated through its successful application to a wide array of materials systems, device configurations and sample morphologies.

We investigate the disorder properties of two-dimensional hole gases in Ge/SiGe heterostructures grown on Ge wafers, using thick SiGe barriers to mitigate the influence of the semiconductor-dielectric interface. Across several heterostructure field effect transistors we measure an average maximum mobility of $(4.4 \pm 0.2) \times 10^{6}~\mathrm{cm^2/Vs}$ at a saturation density of $(1.72 \pm 0.03) \times 10^{11}~\mathrm{cm^{-2}}$, corresponding to a long mean free path of $(30 \pm 1)~\mathrm{\mu m}$. The highest measured mobility is $4.68 \times 10^{6}~\mathrm{cm^2/Vs}$. We identify uniform background impurities and interface roughness as the dominant scattering mechanisms limiting mobility in a representative device, and we evaluate a percolation-induced critical density of $(4.5 \pm 0.1)\times 10^{9} ~\mathrm{cm^{-2}}$. This low-disorder heterostructure, according to simulations, may support the electrostatic confinement of holes in gate-defined quantum dots.

New approaches such as selective area growth, where crystal growth is lithographically controlled, allow the integration of bottom-up grown semiconductor nanomaterials in large-scale classical and quantum nanoelectronics. This calls for assessment and optimization of the reproducibility between individual components. We quantify the structural and electronic statistical reproducibility within large arrays of nominally identical selective area growth InAs nanowires. The distribution of structural parameters is acquired through comprehensive atomic force microscopy studies and transmission electron microscopy. These are compared to the statistical distributions of the cryogenic electrical properties of 256 individual SAG nanowire field effect transistors addressed using cryogenic multiplexer circuits. Correlating measurements between successive thermal cycles allows distinguishing between the contributions of surface impurity scattering and fixed structural properties to device reproducibility. The results confirm the potential of SAG nanomaterials, and the methodologies for quantifying statistical metrics are essential for further optimization of reproducibility.

Bottom-up grown nanomaterials play an integral role in the development of quantum technologies. Among these, semiconductor nanowires (NWs) are widely used in proof-of-principle experiments, however, difficulties in parallel processing of conventionally-grown NWs makes scalability unfeasible. Here, we harness selective area growth (SAG) to remove this road-block. We demonstrate large scale integrated SAG NW circuits consisting of 512 channel multiplexer/demultiplexer pairs, incorporating thousands of interconnected SAG NWs operating under deep cryogenic conditions. Multiplexers enable a range of new strategies in quantum device research and scaling by increase the device count while limiting the number of connections between room-temperature control electronics and the cryogenic samples. As an example of this potential we perform a statistical characterization of large arrays of identical SAG quantum dots thus establishing the feasibility of applying cross-bar gating strategies for efficient scaling of future SAG quantum circuits., Comment: Main text and Supplementary Information

Hybrid semiconductor/superconductor nanowires constitute a pervasive platform for studying gate-tunable superconductivity and the emergence of topological behavior. Their low-dimensionality and crystal structure flexibility facilitate novel heterostructure growth and efficient material optimization; crucial prerequisites for accurately constructing complex multi-component quantum materials. Here, we present an extensive optimization of Sn growth on InSb, InAsSb and InAs nanowires. We demonstrate how the growth conditions and the crystal structure/symmetry of the semiconductor drive the formation of either semi-metallic $\mathrm{\alpha-Sn}$ or superconducting $\mathrm{\beta-Sn}$. For InAs nanowires, we obtain phase-pure, superconducting $\mathrm{\beta-Sn}$ shells. However, for InSb and InAsSb nanowires, an initial epitaxial $\mathrm{\alpha-Sn}$ phase evolves into a polycrystalline shell of coexisting $\mathrm{\alpha}$ and $\mathrm{\beta}$ phases, where the $\beta/\alpha$ volume ratio increases with Sn shell thickness. Whether these nanowires exhibit superconductivity or not critically relies on the $\mathrm{\beta-Sn}$ content. Therefore, this work provides key insights into Sn phase control on a variety of semiconductors, with consequences for the yield of superconducting hybrids suitable for generating topological systems., Comment: 12 pages, 5 figures

Light-harvesting complexes in natural photosynthetic systems, such as those in purple bacteria, consist of photo-reactive chromophores embedded in densely packed "antenna" systems organized in well-defined nanostructures. In the case of purple bacteria, the chromophore antennas are composed of natural J-aggregates such as bacteriochlorophylls and carotenoids. Inspired by the molecular composition of such biological systems, we create a library of organic materials composed of densely packed J-aggregates in a polymeric matrix, in which the matrix mimics a protein scaffold. This library of organic materials shows polaritonic properties which can be tuned from the visible to the infrared by choice of the model molecule. Inspired by the molecular architecture of the light-harvesting complexes of \textit{Rhodospirillum molischianum} bacteria, we study the light-matter interactions of J-aggregate-based nanorings with similar dimensions to the analogous natural nanoscale architectures. Electromagnetic simulations show that these nanorings of J-aggregates can act as resonators, with subwavelength confinement of light while concentrating the electric field in specific regions. These results open the door to bio-inspired building blocks for all-organic metamaterials while offering a new perspective on light-matter interactions at the nanoscale in densely packed organic matter in biological organisms including photosynthetic organelles., Comment: Including supplementary information

Optical Tamm States (OTS) are confined optical modes that can occur at the interface between two highly reflective structures. However, due to the strong reflectance required, their implemen-tation with highly processable and metal-free flexible materials has proven challenging. Herein, we develop the first structure supporting OTS based only on organic polymeric materials, demon-strating a photonic platform based on non-critical, widely available, and easily processable mate-rials. The structures fabricated present large areas and consist of a narrowband multi-layered polymeric Distributed Bragg Reflector (DBR) followed by a thin film of J-aggregate molecular exci-tonic material that can act as a highly reflective surface within a narrowband range. We take ad-vantage of the narrowband spectral response of the DBR and of the reflective molecular layer to tune the OTS band by varying the periodicity of the multilayer, opening the door for the fabrica-tion of OTS structures based on lightweight integrable excitonic devices with cost-effective proce-dures., Comment: 15 pages, 8 (3+5) figures, supplementary information included

The co-integration of spin, superconducting, and topological systems is emerging as an exciting pathway for scalable and high-fidelity quantum information technology. High-mobility planar germanium is a front-runner semiconductor for building quantum processors with spin-qubits, but progress with hybrid superconductor-semiconductor devices is hindered because obtaining a superconducting gap free of subgap states (hard gap) has proven difficult. Here we solve this challenge by developing a low-disorder, oxide-free interface between high-mobility planar germanium and a germanosilicide parent superconductor. This superconducting contact is formed by the thermally-activated solid phase reaction between a metal (Pt) and the semiconductor heterostructure (Ge/SiGe). Electrical characterization reveals near-unity transparency in Josephson junctions and, importantly, a hard induced superconducting gap in quantum point contacts. Furthermore, we demonstrate phase control of a Josephson junction and study transport in a gated two-dimensional superconductor-semiconductor array towards scalable architectures. These results expand the quantum technology toolbox in germanium and provide new avenues for exploring monolithic superconductor-semiconductor quantum circuits towards scalable quantum information processing.

Hybrid semiconductor-superconductor devices hold great promise for realizing topological quantum computing with Majorana zero modes (MZMs). However, multiple claims of Majorana detection, based on either tunneling or Coulomb blockade (CB) spectroscopy, remain disputed. Here we devise an experimental protocol that allows to perform both types of measurements on the same hybrid island by adjusting its charging energy via tunable junctions to the normal leads. This method reduces ambiguities of Majorana detections by checking the consistency between CB spectroscopy and zero bias peaks (ZBPs) in non-blockaded transport.Specifically, we observe junction-dependent, even-odd modulated, single-electron CB peaks in InAs/Al hybrid nanowires (NWs) without concomitant low-bias peaks in tunneling spectroscopy. We provide a theoretical interpretation of the experimental observations in terms of low-energy, longitudinally-confined island states rather than overlapping Majorana modes. Our results highlight the importance of combined measurements on the same device for the identification of topological MZMs.

Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind over-the-air convolution and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call 'AirNN'. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating over-the-air convolutions. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.

Selective area growth (SAG) of nanowires and networks promise a route toward scalable electronics, photonics and quantum devices based on III-V semiconductor materials. The potential of high-mobility SAG nanowires however is not yet fully realized, since interfacial roughness, misfit dislocations at the nanowire/substrate interface and non-uniform composition due to material intermixing all scatter electrons. Here, we explore SAG of highly lattice-mismatched InAs nanowires on insulating GaAs(001) substrates and address these key challenges. Atomically smooth nanowire/substrate interfaces are achieved with the use of atomic hydrogen (a-H) as an alternative to conventional thermal annealing for the native oxide removal. The problem of high lattice mismatch is addressed through an In$_x$Ga$_{1-x}$As buffer layer introduced between the InAs transport channel and the GaAs substrate. The Ga-In material intermixing observed in both the buffer layer and the channel is inhibited via careful tuning of the growth temperature. Performing scanning transmission electron microscopy and x-ray diffraction analysis along with low-temperature transport measurements we show that optimized In-rich buffer layers promote high quality InAs transport channels with the field-effect electron mobility over~10000~cm$^2$V$^{-1}$s$^{-1}$. This is twice as high as for non-optimized samples and among the highest reported for InAs selective area grown nanostructures., Comment: Main text: 9 pages, 5 figures Supporting Information is available at https://erda.ku.dk/archives/d3b11c9d399dd9a715c5e2c84870e3c4/published-archive.html

Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards spend up to several milliseconds exploring different sector combinations to identify the beam pair with the highest SNR. In this paper, we propose a machine learning (ML) approach with two sequential convolutional neural networks (CNN) that uses out-of-band information, in the form of camera images, to (i) rapidly identify the locations of the transmitter and receiver nodes, and then (ii) return the optimal beam pair. We experimentally validate this intriguing concept for indoor settings using the NI 60GHz mmwave transceiver. Our results reveal that our ML approach reduces beamforming related exploration time by 93% under different ambient lighting conditions, with an error of less than 1% compared to the time-intensive deterministic method defined by the current standards.

Hole gases in planar germanium can have high mobilities in combination with strong spin-orbit interaction and electrically tunable g-factors, and are therefore emerging as a promising platform for creating hybrid superconductor-semiconductor devices. A key challenge towards hybrid Ge-based quantum technologies is the design of high-quality interfaces and superconducting contacts that are robust against magnetic fields. In this work, by combining the assets of aluminum, which provides good contact to the Ge, and niobium, which has a significant superconducting gap, we demonstrate highly transparent low-disordered JoFETs with relatively large \IcRn \ products that are capable of withstanding high magnetic fields. We furthermore demonstrate the ability of phase-biasing individual JoFETs, opening up an avenue to explore topological superconductivity in planar Ge. The persistence of superconductivity in the reported hybrid devices beyond 1.8 Tesla paves the way towards integrating spin qubits and proximity-induced superconductivity on the same chip.

Nanowires can serve as flexible substrates for hybrid epitaxial growth on selected facets, allowing for design of heterostructures with complex material combinations and geometries. In this work we report on hybrid epitaxy of semiconductor - ferromagnetic insulator - superconductor (InAs/EuS/Al) nanowire heterostructures. We study the crystal growth and complex epitaxial matching of wurtzite InAs / rock-salt EuS interfaces as well as rock-salt EuS / face-centered cubic Al interfaces. Because of the magnetic anisotropy originating from the nanowire shape, the magnetic structure of the EuS phase are easily tuned into single magnetic domains. This effect efficiently ejects the stray field lines along the nanowires. With tunnel spectroscopy measurements of the density of states, we show the material has a hard induced superconducting gap, and magnetic hysteretic evolution which indicates that the magnetic exchange fields are not negligible. These hybrid nanowires fulfil key material requirements for serving as a platform for spin-based quantum applications, such as scalable topological quantum computing., Comment: 15 pages, 5 figures

Hybrid semiconductor-ferromagnetic insulator heterostructures are interesting due to their tunable electronic transport, self-sustained stray field and local proximitized magnetic exchange. In this work, we present lattice matched hybrid epitaxy of semiconductor - ferromagnetic insulator InAs/EuS heterostructures and analyze the atomic-scale structure as well as their electronic and magnetic characteristics. The Fermi level at the InAs/EuS interface is found to be close to the InAs conduction band and in the bandgap of EuS, thus preserving the semiconducting properties. Both neutron and X-ray reflectivity measurements show that the ferromagnetic component is mainly localized in the EuS thin film with a suppression of the Eu moment in the EuS layer nearest the InAs. Induced moments in the adjacent InAs layers were not detected although our ab initio calculations indicate a small exchange field in the InAs layer. This work presents a step towards realizing high quality semiconductor - ferromagnetic insulator hybrids, which is a critical requirement for development of various quantum and spintronic applications without external magnetic fields.

Topological qubits based on Majorana fermions have the potential to revolutionize the emerging field of quantum computing by making information processing significantly more robust to decoherence. Nanowires (NWs) are a promising medium for hosting these kinds of qubits, though branched NWs are needed to perform qubit manipulations. Here we report gold-free templated growth of III-V NWs by molecular beam epitaxy using an approach that enables patternable and highly regular branched NW arrays on a far greater scale than what has been reported thus far. Our approach relies on the lattice-mismatched growth of InAs on top of defect-free GaAs nanomembranes (NMs) yielding laterally-oriented, low-defect InAs and InGaAs NWs whose shapes are determined by surface and strain energy minimization. By controlling NM width and growth time, we demonstrate the formation of compositionally graded NWs with cross-sections less than 50 nm. Scaling the NWs below 20 nm leads to the formation of homogenous InGaAs NWs which exhibit phase-coherent, quasi-1D quantum transport as shown by magnetoconductance measurements. These results are an important advance towards scalable topological quantum computing., Comment: 20 pages, 3 figures

III-V semiconductor nanowires have shown great potential in various quantum transport experiments. However, realizing a scalable high-quality nanowire-based platform that could lead to quantum information applications has been challenging. Here, we study the potential of selective area growth by molecular beam epitaxy of InAs nanowire networks grown on GaAs-based buffer layers. The buffered geometry allows for substantial elastic strain relaxation and a strong enhancement of field effect mobility. We show that the networks possess strong spin-orbit interaction and long phase coherence lengths with a temperature dependence indicating ballistic transport. With these findings, and the compatibility of the growth method with hybrid epitaxy, we conclude that the material platform fulfills the requirements for a wide range of quantum experiments and applications.