A superconducting thermal switch with ultrahigh impedance for interfacing superconductors to semiconductors

Structural and Electronic Properties of the Magnetic and Nonmagnetic X0.125Mg0.875B2 (X = Nb, Ni, Fe) Materials: A DFT/HSE06 Approach to Investigate Superconductor Behavior

MgB2 material has a simple composition and structure that is well-reported and characterized. This material has been widely studied and applied in the last 20 years as a superconductor in wire devices and storage material for H in the hydride form. MgB2 doped with transition metals improves the superconductor behavior, such as the critical temperature (T cs) or critical current (J sc) for the superconducting state. The results obtained in this manuscript indicate that Nb-, Fe-, and Ni-doping in the Mg site leads to a contraction of the unit cell through the spin polarization on the electronic resonance of the boron layer. Fe and Ni transition metals doping perturb the electronic resonance because of stronger dopant-boron bonds. The unpaired electrons are transferred from 3d orbitals to the empty 2p z orbitals of the boron atoms, locating α electrons in the σ bonds and β electrons in the π orbitals. The observed influence of magnetic dopants on MgB2 enables the proposal of an electronic mechanism to explain the spin polarization of boron hexagonal rings.

Photorefraction Simulates Well the Plasticity of Neural Synaptic Connections

In recent years, the need for systems capable of achieving the dynamic learning and information storage efficiency of the biological brain has led to the emergence of neuromorphic research. In particular, neuromorphic optics was born with the idea of reproducing the functional and structural properties of the biological brain. In this context, solitonic neuromorphic research has demonstrated the ability to reproduce dynamic and plastic structures capable of learning and storing through conformational changes in the network. In this paper, we demonstrate that solitonic neural networks are capable of mimicking the functional behaviour of biological neural tissue, in terms of synaptic formation procedures and dynamic reinforcement.

Machine learning-powered compact modeling of stochastic electronic devices using mixture density networks

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capturing the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

A superconducting nanowire single-photon camera with 400,000 pixels

For the past 50 years, superconducting detectors have offered exceptional sensitivity and speed for detecting faint electromagnetic signals in a wide range of applications. These detectors operate at very low temperatures and generate a minimum of excess noise, making them ideal for testing the non-local nature of reality1,2, investigating dark matter3,4, mapping the early universe5-7 and performing quantum computation8-10 and communication11-14. Despite their appealing properties, however, there are at present no large-scale superconducting cameras-even the largest demonstrations have never exceeded 20,000 pixels15. This is especially true for superconducting nanowire single-photon detectors (SNSPDs)16-18. These detectors have been demonstrated with system detection efficiencies of 98.0% (ref. 19), sub-3-ps timing jitter20, sensitivity from the ultraviolet21 to the mid-infrared22 and microhertz dark-count rates3, but have never achieved an array size larger than a kilopixel23,24. Here we report on the development of a 400,000-pixel SNSPD camera, a factor of 400 improvement over the state of the art. The array spanned an area of 4 × 2.5 mm with 5 × 5-μm resolution, reached unity quantum efficiency at wavelengths of 370 nm and 635 nm, counted at a rate of 1.1 × 105 counts per second (cps) and had a dark-count rate of 1.0 × 10-4 cps per detector (corresponding to 0.13 cps over the whole array). The imaging area contains no ancillary circuitry and the architecture is scalable well beyond the present demonstration, paving the way for large-format superconducting cameras with near-unity detection efficiencies across a wide range of the electromagnetic spectrum.

All optical operation of a superconducting photonic interface

Quantum photonic processing via electro-optic components typically requires electronic links across different operation environments, especially when interfacing cryogenic components such as superconducting single photon detectors with room-temperature control and readout electronics. However, readout and driving electronics can introduce detrimental parasitic effects. Here we show an all-optical control and readout of a superconducting nanowire single photon detector (SNSPD), completely electrically decoupled from room temperature electronics. We provide the operation power for the superconducting detector via a cryogenic photodiode, and readout single photon detection signals via a cryogenic electro-optic modulator in the same cryostat. This method opens the possibility for control and readout of superconducting circuits, and feedforward for photonic quantum computing.

Emerging 2D Metal Oxides: From Synthesis to Device Integration

2D metal oxides have aroused increasing attention in the field of electronics and optoelectronics due to their intriguing physical properties. In this review, an overview of recent advances on synthesis of 2D metal oxides and their electronic applications is presented. First, the tunable physical properties of 2D metal oxides that relate to the structure (various oxidation-state forms, polymorphism, etc.), crystallinity and defects (anisotropy, point defects, and grain boundary), and thickness (quantum confinement effect, interfacial effect, etc.) are discussed. Then, advanced synthesis methods for 2D metal oxides besides mechanical exfoliation are introduced and classified into solution process, vapor-phase deposition, and native oxidation on a metal source. Later, the various roles of 2D metal oxides in widespread applications, i.e., transistors, inverters, photodetectors, piezotronics, memristors, and potential applications (solar cell, spintronics, and superconducting devices) are discussed. Finally, an outlook of existing challenges and future opportunities in 2D metal oxides is proposed.

Signatures of Gate-Driven Out-of-Equilibrium Superconductivity in Ta/InAs Nanowires

Understanding the microscopic origin of the gate-controlled supercurrent (GCS) in superconducting nanobridges is crucial for engineering superconducting switches suitable for a variety of electronic applications. The origin of GCS is controversial, and various mechanisms have been proposed to explain it. In this work, we have investigated the GCS in a Ta layer deposited on the surface of InAs nanowires. Comparison between switching current distributions at opposite gate polarities and between the gate dependence of two opposite side gates with different nanowire-gate spacings shows that the GCS is determined by the power dissipated by the gate leakage. We also found a substantial difference between the influence of the gate and elevated bath temperature on the magnetic field dependence of the supercurrent. Detailed analysis of the switching dynamics at high gate voltages shows that the device is driven into the multiple phase slips regime by high-energy fluctuations arising from the leakage current.

A three-terminal magnetic thermal transistor

Three-terminal thermal analogies to electrical transistors have been proposed for use in thermal amplification, thermal switching, or thermal logic, but have not yet been demonstrated experimentally. Here, we design and fabricate a three-terminal magnetic thermal transistor in which the gate temperature controls the source-drain heat flow by toggling the source-drain thermal conductance from ON to OFF. The centimeter-scale thermal transistor uses gate-temperature dependent magnetic forces to actuate motion of a thermally conducting shuttle, providing thermal contact between source and drain in the ON state while breaking contact in the OFF state. We measure source-drain thermal switch ratios of 109 ± 44 in high vacuum with gate switching temperatures near 25 °C. Thermal measurements show that small heat flows into the gate can be used to drive larger heat flows from source to drain, and that the switching is reversible over >150 cycles. Proof-of-concept thermal circuit demonstrations show that magnetic thermal transistors can enable passive or active heat flow routing or can be combined to create Boolean thermal logic gates. This work will allow thermal researchers to explore the behavior of nonlinear thermal circuits using three-terminal transistors and will motivate further research developing thermal transistors for advanced thermal control.

Out-of-equilibrium phonons in gated superconducting switches

Recent experiments have suggested that superconductivity in metallic nanowires can be suppressed by the application of modest gate voltages. The source of this gate action has been debated and either attributed to an electric-field effect or to small leakage currents. Here we show that the suppression of superconductivity in titanium nitride nanowires on silicon substrates does not depend on the presence or absence of an electric field at the nanowire, but requires a current of high-energy electrons. The suppression is most efficient when electrons are injected into the nanowire, but similar results are obtained when electrons are passed between two remote electrodes. This is explained by the decay of high-energy electrons into phonons, which propagate through the substrate and affect superconductivity in the nanowire by generating quasiparticles. By studying the switching probability distribution of the nanowire, we also show that high-energy electron emission leads to a much broader phonon energy distribution compared with the case where superconductivity is suppressed by Joule heating near the nanowire.

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.

A superconducting switch actuated by injection of high-energy electrons

Recent experiments with metallic nanowires devices seem to indicate that superconductivity can be controlled by the application of electric fields. In such experiments, critical currents are tuned and eventually suppressed by relatively small voltages applied to nearby gate electrodes, at odds with current understanding of electrostatic screening in metals. We investigate the impact of gate voltages on superconductivity in similar metal nanowires. Varying materials and device geometries, we study the physical mechanism behind the quench of superconductivity. We demonstrate that the transition from superconducting to resistive state can be understood in detail by tunneling of high-energy electrons from the gate contact to the nanowire, resulting in quasiparticle generation and, at sufficiently large currents, heating. Onset of critical current suppression occurs below gate currents of 100fA, which are challenging to detect in typical experiments.

A recent finding of tuning critical current in metallic nanowires by application of small gate voltages seems at odds with general understanding. Here, Ritter et al. study similar nanowires and link the origin of the critical current suppression to tunneling of few high-energy electrons between gate and nanowire, ruling out direct tuning by electric fields.

Roadmap on emerging hardware and technology for machine learning

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Photonic Readout of Superconducting Nanowire Single Photon Counting Detectors

Scalable, low power, high speed data transfer between cryogenic (0.1–4 K) and room temperature environments is essential for the realization of practical, large-scale systems based on superconducting technologies. A promising approach to overcome the limitations of conventional wire-based readout is the use of optical fiber communication. Optical fiber presents a 100–1,000x lower heat load than conventional electrical wiring, relaxing the requirements for thermal anchoring, and is also immune to electromagnetic interference, which allows routing of sensitive signals with improved robustness to noise and crosstalk. Most importantly, optical fibers allow for very high bandwidth densities (in the Tbps/mm² range) by carrying multiple signals through the same physical fiber (Wavelength Division Multiplexing, WDM). Here, we demonstrate for the first time optical readout of a superconducting nanowire single-photon detector (SNSPD) directly coupled to a CMOS photonic modulator, without the need for an interfacing device. By operating the modulator in the forward bias regime at a temperature of 3.6 K, we achieve very high modulation efficiency (1,000–10,000 pm/V) and a low input impedance of 500 Ω with a low power dissipation of 40 μW. This allows us to obtain optical modulation with the low, millivolt-level signal generated by the SNSPD.

A Superconducting Binary Encoder with Multigate Nanowire Cryotrons

Many classic and quantum devices need to operate at cryogenic temperatures, demanding advanced cryogenic digital electronics for processing the input and output signals on a chip to extend their scalability and performance. Here, we report a superconducting binary encoder with ultralow power dissipation and ultracompact size. We introduce a multigate superconducting nanowire cryotron (nTron) that functions as an 8-input OR gate within a footprint of approximately 0.5 μm². Four cryotrons compose a 4-bit encoder that has a bias margin of 18.9%, an operation speed greater than 250 MHz, an average switching jitter of 75 ps, and a power dissipation of less than 1 μW. We apply this encoder to read out a superconducting-nanowire single-photon detector array whose pixel location is digitized into a 4-bit binary address. The small size of the nanowire combined with the low power dissipation makes nTrons promising for future monolithic integration.