Search for: All records

Neuromorphic hardware promises to revolutionize information technology with brain-inspired parallel processing, in-memory computing, and energy-efficient implementation of artificial intelligence and machine learning. In particular, two-dimensional (2D) memtransistors enable gate-tunable non-volatile memory, bio-realistic synaptic phenomena, and atomically thin scaling. However, previously reported 2D memtransistors have not achieved low operating voltages without compromising gate-tunability. Here, we overcome this limitation by demonstrating MoS2 memtransistors with short channel lengths < 400 nm, low operating voltages < 1 V, and high field-effect switching ratios > 10,000 while concurrently achieving strong memristive responses. This functionality is realized by fabricating back-gated memtransistors using highly polycrystalline monolayer MoS2 channels on high-κ Al2O3 dielectric layers. Finite-element simulations confirm enhanced electrostatic modulation near the channel contacts, which reduces operating voltages without compromising memristive or field-effect switching. Overall, this work demonstrates a pathway for reducing the size and power consumption of 2D memtransistors as is required for ultrahigh-density integration. 

Unlike the well-studied and technologically advanced Group III-V and Group II-VI compound semiconductor alloys, alloys of ternary metal oxide semiconductors have only recently begun to receive widespread attention. Here, we describe the effect of alkaline earth metal substitution on the optical, electronic, and photoelectrochemical (PEC) properties of copper metavanadate (CuV₂O₆). As a host, the Cu-V-O compound family presents a versatile framework to develop such composition-property correlations. Alloy compositions of A_0.1Cu_0.9V₂O₆(A = Mg, Ca) photoanodes were synthesized via a time and energy-efficient solution combustion synthesis (SCS) method. The effect of introducing alkaline earth metals (Mg, Ca) on the crystal structure, microstructure, electronic, and optical properties of copper metavanadates was investigated by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), diffuse reflectance spectroscopy (DRS), transmission electron microscopy (TEM), and Raman spectroscopy. The PXRD, TEM, and Raman spectroscopy data demonstrated the polycrystalline powder samples to be mutually soluble, solid solutions of copper and alkaline earth metal metavanadates and not simple mixtures of these compounds. The DRS data showed a systematic decrease in the optical bandgap with Cu incorporation. These trends were corroborated by electronic band structure calculations. Finally, the PEC properties exhibited a strong dependence on the alloy composition, pointing to possible applicability in solar water splitting, heterogeneous photocatalysis, phosphor lighting/displays, and photovoltaic devices.

 

Van der Waals materials with long-range magnetic order show a range of correlated phenomena that could be of use in the development of optoelectronic and spintronic applications. Magnetically ordered van der Waals semiconductors with spin-polarized currents are, in particular, sensitive to external stimuli such as strain, electrostatic fields, magnetic fields and electromagnetic radiation. Their combination of two-dimensional magnetic order, semiconducting band structure and weak dielectric screening means that these materials could be used to create novel atomically thin opto-spintronic devices. Here we explore the development of van der Waals opto-spintronics. We examine the interplay between optical, magnetic and electronic excitations in van der Waals magnetic semiconductors, and explore the control of their magnetization via external stimuli. We consider fabrication and passivation strategies for the practical handling and design of opto-spintronic devices. We also explore potential opto-spintronic device architectures and applications, which include magnonics, quantum transduction, neuromorphic computing and non-volatile memory. 

The growth of layered 2D compounds is a key ingredient in finding new phenomena in quantum materials, optoelectronics, and energy conversion. Here, we report SnP₂Se₆, a van der Waals chiral (R3 space group) semiconductor with an indirect bandgap of 1.36 to 1.41 electron volts. Exfoliated SnP₂Se₆flakes are integrated into high-performance field-effect transistors with electron mobilities >100 cm²/Vs and on/off ratios >10⁶at room temperature. Upon excitation at a wavelength of 515.6 nanometer, SnP₂Se₆phototransistors show high gain (>4 × 10⁴) at low intensity (≈10⁻⁶W/cm²) and fast photoresponse (< 5 microsecond) with concurrent gain of ≈52.9 at high intensity (≈56.6 mW/cm²) at a gate voltage of 60 V across 300-nm-thick SiO₂dielectric layer. The combination of high carrier mobility and the non-centrosymmetric crystal structure results in a strong intrinsic bulk photovoltaic effect; under local excitation at normal incidence at 532 nm, short circuit currents exceed 8 mA/cm²at 20.6 W/cm².

 

We discuss how a dual-gated memtransistor crossbar can accelerate the extraction of the Transformer’s attention scores. A memtransistor is a novel two-dimensional material-based device that offers non-volatile programmability and gate tunability. Leveraging these attributes, we demonstrate the extraction of quadratic-order products on a single memtransistor and the single-step extraction of attention scores without inferring intermediate query/key vectors. The query/key-free processing of memtransistor-based attention scoring results in 2.37× lower energy with less than half crossbar cells. 

Antiferromagnetic (AFM) materials are a pathway to spintronic memory and computing devices with unprecedented speed, energy efficiency, and bit density. Realizing this potential requires AFM devices with simultaneous electrical writing and reading of information, which are also compatible with established silicon‐based manufacturing. Recent experiments have shown tunneling magnetoresistance (TMR) readout in epitaxial AFM tunnel junctions. However, these TMR structures are not grown using a silicon‐compatible deposition process, and controlling their AFM order required external magnetic fields. Here are shown three‐terminal AFM tunnel junctions based on the noncollinear antiferromagnet PtMn₃, sputter‐deposited on silicon. The devices simultaneously exhibit electrical switching using electric currents, and electrical readout by a large room‐temperature TMR effect. First‐principles calculations explain the TMR in terms of the momentum‐resolved spin‐dependent tunneling conduction in tunnel junctions with noncollinear AFM electrodes.

 

The convergence of topology and correlations represents a highly coveted realm in the pursuit of novel quantum states of matter [1, 2]. Introducing electron correlations to a quantum spin Hall (QSH) insulator can lead to the emergence of a fractional topological insulator and other exotic time-reversal-symmetric topological order [3– 10], not possible in quantum Hall and Chern insulator systems. However, the QSH insulator with quantized edge conductance remains rare, let alone that with significant correlations. In this work, we report a novel dual QSH insulator within the intrinsic monolayer crystal of TaIrTe4, arising from the interplay of its single-particle topology and density-tuned electron correlations. At charge neutrality, monolayer TaIrTe4 demonstrates the QSH insulator that aligns with single-particle band structure calculations, manifesting enhanced nonlocal transport and quantized helical edge conductance. Interestingly, upon introducing electrons from charge neutrality, TaIrTe4 only shows metallic behavior in a small range of charge densities but quickly goes into a new insulating state, entirely unexpected based on TaIrTe4’s single-particle band structure. This insulating state could arise from a strong electronic instability near the van Hove singularities (VHS), likely leading to a charge density wave (CDW). Remarkably, within this correlated insulating gap, we observe a resurgence of the QSH state, marked by the revival of nonlocal transport and quantized helical edge conduction. Our observation of helical edge conduction in a CDW gap could bridge spin physics and charge orders. The discovery of a dual QSH insulator introduces a new method for creating topological flat minibands via CDW superlattices, which offer a promising platform for exploring time-reversal-symmetric fractional phases and electromagnetism [3–5, 11, 12]. 

Advances in algorithms and low-power computing hardware imply that machine learning is of potential use in off-grid medical data classification and diagnosis applications such as electrocardiogram interpretation. However, although support vector machine algorithms for electrocardiogram classification show high classification accuracy, hardware implementations for edge applications are impractical due to the complexity and substantial power consumption needed for kernel optimization when using conventional complementary metal–oxide–semiconductor circuits. Here we report reconfigurable mixed-kernel transistors based on dual-gated van der Waals heterojunctions that can generate fully tunable individual and mixed Gaussian and sigmoid functions for analogue support vector machine kernel applications. We show that the heterojunction-generated kernels can be used for arrhythmia detection from electrocardiogram signals with high classification accuracy compared with standard radial basis function kernels. The reconfigurable nature of mixed-kernel heterojunction transistors also allows for personalized detection using Bayesian optimization. A single mixed-kernel heterojunction device can generate the equivalent transfer function of a complementary metal–oxide–semiconductor circuit comprising dozens of transistors and thus provides a low-power approach for support vector machine classification applications. 

The increasing complexity of deep learning systems has pushed conventional computing technologies to their limits. While the memristor is one of the prevailing technologies for deep learning acceleration, it is only suited for classical learning layers where only two operands, namely weights and inputs, are processed simultaneously. Meanwhile, to improve the computational efficiency of deep learning for emerging applications, a variety of non-traditional layers requiring concurrent processing of many operands are becoming popular. For example, hypernetworks improve their predictive robustness by simultaneously processing weights and inputs against the application context. Two-electrode memristor grids cannot directly map emerging layers’ higher-order multiplicative neural interactions. Addressing this unmet need, we present crossbar processing using dual-gated memtransistors based on two-dimensional semiconductor MoS 2 . Unlike the memristor, the resistance states of memtransistors can be persistently programmed and can be actively controlled by multiple gate electrodes. Thus, the discussed memtransistor crossbar enables several advanced inference architectures beyond a conventional passive crossbar. For example, we show that sneak paths can be effectively suppressed in memtransistor crossbars, whereas they limit size scalability in a passive memristor crossbar. Similarly, exploiting gate terminals to suppress crossbar weights dynamically reduces biasing power by ∼20% in memtransistor crossbars for a fully connected layer of AlexNet. On emerging layers such as hypernetworks, collocating multiple operations within the same crossbar cells reduces operating power by ∼ 15 × on the considered network cases.