Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS2 transistors

Probabilistic computing is a computing scheme that offers a more efficient approach than conventional complementary metal-oxide-semiconductor (CMOS)-based logic in a variety of applications ranging from optimization to Bayesian inference, and invertible Boolean logic. The probabilistic bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires tunable stochasticity; by coupling low-barrier stochastic magnetic tunnel junctions (MTJs) with a transistor circuit, a compact implementation is achieved. In this work, by combining stochastic MTJs with 2D-MoS2 field-effect transistors (FETs), we demonstrate an on-chip realization of a p-bit building block displaying voltage-controllable stochasticity. Supported by circuit simulations, we analyze the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component influence the overall p-bit output. While the current approach has not reached the level of maturity required to compete with CMOS-compatible MTJ technology, the design rules presented in this work are valuable for future experimental implementations of scaled on-chip p-bit networks with reduced footprint.

Nonvolatile Reconfigurable Logic Device Based on Photoinduced Interfacial Charge Trapping in van der Waals Gap

Due to the increasing complexity in miniaturization of electronic devices, reconfigurable field-effect transistors (RFETs) have emerged as a solution. Although the foundational concepts of RFETs have matured over two decades, ongoing breakthroughs are needed to address challenges such as improving the device performance as well as achieving balanced symmetry between n-type and p-type transport modes with long-term stability. Herein, we present a nonvolatile WSe2-based RFET that utilizes photoassisted interfacial charge trapping at the h-BN and SiO2 interface. Unlike typical RFETs with two gate electrodes, our RFETs achieved polarity control with a single operating gate activated exclusively under white-light exposure. The threshold voltage was tunable, ranging from 27.4 (-31.6 V) to 0.9 (+19.5 V), allowing selective activation of n-type (p-type) operation at VGS = 0 V. Additionally, our WSe2-based RFETs show superior repeatability and long-term stability. Leveraging these advantages, various reconfigurable logic circuits were successfully demonstrated, including complementary inverters and switch circuits as well as pull-up and pull-down circuits, highlighting the potential of WSe2 FETs for future advancements of integrated circuits.

Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems

The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems faces challenges such as high power consumption and limited scalability. Here, we experimentally present an Ising annealing computer based on 80 superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which solves a 70-city traveling salesman problem (TSP, 4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing global annealing scheme, and using efficient algorithm, our SMTJ-based Ising annealer outperforms other Ising schemes in terms of power consumption and energy efficiency. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a cross-bar array architecture for scalable integration using conventional magnetic random-access memories. Our results demonstrate that the SMTJ-based Ising computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.

Perspective: an optoelectronic future for heterogeneous, dendritic computing

With the increasing number of applications reliant on large neural network models, the pursuit of more suitable computing architectures is becoming increasingly relevant. Progress toward co-integrated silicon photonic and CMOS circuits provides new opportunities for computing architectures with high bandwidth optical networks and high-speed computing. In this paper, we discuss trends in neuromorphic computing architecture and outline an optoelectronic future for heterogeneous, dendritic neuromorphic computing.

Tunable stochastic memristors for energy-efficient encryption and computing

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements - security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 ('CuTeHO') ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

Memristor-Based Neuromorphic Chips

In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

High-density via RRAM cell with multi-level setting by current compliance circuits

In this work, multi-level storage in the via RRAM has been first time reported and demonstrated with the standard FinFET CMOS logic process. Multi-level states in via RRAM are achieved by controlling the current compliance during set operations. The new current compliance setting circuits are proposed to ensure stable resistance control when one considers cells under the process variation effect. The improved stability and tightened distributions on its multi-level states on via RRAM have been successfully demonstrated.

Reconfigurable optical logic in silicon platform

In this paper, we present a novel, scalable, and reconfigurable optical switch that performs multiple computational logic functions simultaneously. The free-carrier depletion effect is used to perform non-volatile switching operations due to its high speed and low power consumption. We adopt the concept of optical memory using a phase-change material to realize the non-volatile reconfigurability without a constant power supply, in addition to providing a large operating bandwidth required for reconfigurability. The proposed reconfigurable optical logic architecture is realized in a compact microdisk resonator configuration, utilizing both the carrier-depletion-based modulation and phase-change optical memory. This is the first time these two modulation schemes are implemented in the same optical microdisk for the purpose of reconfigurable optical logic.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

The Integration of Two-Dimensional Materials and Ferroelectrics for Device Applications

In recent years, there has been growing interest in functional devices based on two-dimensional (2D) materials, which possess exotic physical properties. With an ultrathin thickness, the optoelectrical and electrical properties of 2D materials can be effectively tuned by an external field, which has stimulated considerable scientific activities. Ferroelectric fields with a nonvolatile and electrically switchable feature have exhibited enormous potential in controlling the electronic and optoelectronic properties of 2D materials, leading to an extremely fertile area of research. Here, we review the 2D materials and relevant devices integrated with ferroelectricity. This review starts to introduce the background about the concerned themes, namely 2D materials and ferroelectrics, and then presents the fundamental mechanisms, tuning strategies, as well as recent progress of the ferroelectric effect on the optical and electrical properties of 2D materials. Subsequently, the latest developments of 2D material-based electronic and optoelectronic devices integrated with ferroelectricity are summarized. Finally, the future outlook and challenges of this exciting field are suggested.

Reliable synaptic plasticity of InGaZnO transistor with TiO2interlayer

We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2buffer layer. The structure of the synaptic transistor with TiO2inserted between the Ti metal electrode and an IGZO semiconductor channel O2trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems.

Synthesis of Cobalt Particles and Investigation of Their Electromagnetic Wave Absorption Characteristics

As the integration technology for integrated circuit (IC) packaging continues to advance, the issue of electromagnetic interference in IC packaging becomes increasingly prominent. Magnetic materials, acknowledged for their superior electromagnetic absorption capabilities, play a pivotal role in mitigating electromagnetic interference problems. In this study, we employed a liquid-phase reduction method. We prepared three types of cobalt (Co) particles with distinct morphologies. Through variations in the synthesis process conditions, we were able to control the aspect ratio of protrusions on the surface of the Co particles. It was found that the sword-like Co particles exhibit superior electromagnetic wave absorption capabilities, showing a reflection loss value of up to -50.96 dB. Notably, when the coating thickness is only 1.6 mm, the effective absorption bandwidth is extended up to 7.6 GHz. The spatially expansive sword-like Co particles, with their unique structure featuring dipole polarization and interfacial polarization, demonstrated enhanced dielectric and magnetic loss capabilities, concurrently showcasing superior impedance-matching performance.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Latest Advancements in Next-Generation Semiconductors: Materials and Devices for Wide Bandgap and 2D Semiconductors

Semiconductor materials, devices, and systems have become indispensable pillars supporting the modern world, deeply ingrained in various facets of our daily lives [...].

Event-driven adaptive optical neural network

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

Physical probing of quantum energy levels in a single indium arsenide (InAs) quantum dot

Indium arsenide (InAs) quantum dots (QDs) grown by molecular beam epitaxy (EBM) on gallium arsenide (GaAs) substrates have exhibited quantized charge-trapping characteristics. An electric charge can be injected in a single QD by a gold-coated AFM nano-probe placed directly on it using a conductive-mode atomic force microscope (C-AFM). The results revealed separate current-voltage (I-V) curves during consecutive measurements, where the turn-on voltages measured at the subsequent voltage sweeps are incrementally lower than that at the initial sweep. We demonstrate that the charge state of the QD can change over a long enough time by measuring the I-V data on the same QD at different time intervals. Discrete energy states (here, five states) have been observed due to the quantized charge leakage from the QD into the surrounding materials. These quantum states with five energy levels have been verified using quantum theory analysis of the quantum-well with the help of a numerical simulation model, which depends on the QD dimensions. The size of the quantum-well in the model is in good agreement with the actual QD size, whose lateral dimension is confirmed using a scanning electron microscope. At the same time, the height is estimated from the atomic force microscope topography.

All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse

Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.

DTCO optimizes critical path nets to improve chip performance with timing-aware OPC in deep ultraviolet lithography

Design technology co-optimization (DTCO) is a potential approach to tackle the escalating expenses and complexities associated with pitch scaling. This strategy offers a promising solution by minimizing the required design dimensions and mitigating the pitch scaling trend. It is worth noting that lithography has played a significant role in dimensional scaling over time. This paper proposes a DTCO flow to reduce the impact of the process variation (PV) band and edge placement error (EPE). First, we performed the digital back-end design of the high-performance processor and got the test layout; second, we executed timing analysis on the test layout to get the critical path net that affects the chip performance; third, we proposed the timing-aware optimized optical proximity correction (OPC) method to optimize the PV band and EPE by adjusting the weights of critical path net merit points, optimizing the generation of the sub-resolution assistant feature, giving tighter EPE specs for merit points on the critical path net, and placing denser merit points as well as denser breakpoints for the critical path net to obtain greater freedom in the OPC process. Finally, it is verified that our proposed DTCO process can significantly reduce the EPE and lead to a slight decrease in the PV band of the chip while maintaining the same process windows.

Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System

In this paper, we fabricate an ITO/SiN/TaN memristor device and analyze its electrical characteristics for a neuromorphic system. The device structure and chemical properties are investigated using transmission electron microscopy and X-ray photoelectron spectroscopy. Uniform bipolar switching is achieved through DC sweep under a compliance current of 5 mA. Also, the analog reset phenomenon is observed by modulating the reset voltage for long-term memory. Additionally, short-term memory characteristics are obtained by controlling the strength of the pulse response. Finally, bio-inspired synaptic characteristics are emulated using Hebbian learning rules such as spike-rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP). As a result, we believe that the coexistence of short-term and long-term memories in the ITO/SiN/TaN device can provide flexibility in device design in future neuromorphic applications.

Time derivatives via interconnected waveguides

Electromagnetic wave-based analogue computing has become an interesting computing paradigm demonstrating the potential for high-throughput, low power, and parallel operations. In this work, we propose a technique for the calculation of derivatives of temporal signals by exploiting transmission line techniques. We consider multiple interconnected waveguides (with some of them being closed-ended stubs) forming junctions. The transmission coefficient of the proposed structure is then tailored by controlling the length and number of stubs at the junction, such that the differentiation operation is applied directly onto the envelope of an incident signal sinusoidally modulated in the time domain. The physics behind the proposed structure is explained in detail and a full theoretical description of this operation is presented, demonstrating how this technique can be used to calculate higher order or even fractional temporal derivatives. We envision that these results may enable the development of further time domain wave-based analogue processors by exploiting waveguide junctions, opening new opportunities for wave-based single operators and systems.

Mechanisms for successful management of enterprise resource planning from user information processing and system quality perspective

Enterprise resource planning (ERP) systems are now ubiquitous in modern organizations. A number of previous studies have focused only on system factors and perceptions, there is a noticeable shortfall in research that concurrently addresses technological factors and human roles in explaining user satisfaction. This study aimed to identify these variables from the perspectives of information systems, technology, and human participation, thereby addressing this knowledge gap. The focus of the study was a large shipbuilding and marine company utilizing an ERP system. The participants, a sample of 234 ERP users, were carefully selected by the company's executives and practitioners, and data was collected through online questionnaires. They were selected through purposive sampling from among employees who use ERP systems in large-scale shipbuilding and marine engineering companies. The study aimed to clarify the relationships between user satisfaction and perceived ease of use, perceived usefulness, system quality, service quality, participation, and information quality. A partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data. The results indicated that perceived ease of use, system quality, service quality, and participation positively influenced user satisfaction, whereas perceived usefulness did not have a significant impact. Interestingly, participation was found to lessen the effects of perceived usefulness on satisfaction. The findings of this study suggest that to enhance ERP user satisfaction, managers should strive to make the ERP system easy-to-use and stable, encourage employee participation in the decision-making process, and bolster the role of the support team. It should be noted, however, that the study has limitations as it did not consider all possible factors, such as training and support. Future research should take a broader view of the variables involved in the operation of an enterprise-wide information system.

Optical polarization perturbed by shear strains of ultrasonic bulk waves in anisotropic semiconductors: Multiphysics modeling and optoacoustic validation

Characterization of lattice properties of monocrystalline semiconductors (MS) has been rapidly advanced. Of particular interest is the use of shear strains induced by optoacoustic-bulk-waves. However, this technique has been hindered owing to the lack of quantitative correlations between optoacoustic-bulk-waves-induced shear strains and anisotropic photoelasticity of MS. Motivated by this, a multiphysics model is developed to interrogate the coupling phenomena and interaction between optical polarization and shear strains in MS. With the model, perturbation to the polarization of a monochromatic laser beam, upon interacting with optoacoustic waves in MS, is scrutinized quantitatively. Experimental results are in agreement with those from the model, both revealing the polarization perturbed by shear strains quantitatively depends on the crystal orientation and crystal-structure-related symmetry, which are jointly governed by mechanical/photoelastic/optical anisotropies of MS. The approach has paved a new way for selectively acquiring high-sensitivity shear components of optoacoustic-ultrasonic-waves for in situ, high-definition characterization of anisotropic MS.

Materials Quest for Advanced Interconnect Metallization in Integrated Circuits

Integrated circuits (ICs) are challenged to deliver historically anticipated performance improvements while increasing the cost and complexity of the technology with each generation. Front-end-of-line (FEOL) processes have provided various solutions to this predicament, whereas the back-end-of-line (BEOL) processes have taken a step back. With continuous IC scaling, the speed of the entire chip has reached a point where its performance is determined by the performance of the interconnect that bridges billions of transistors and other devices. Consequently, the demand for advanced interconnect metallization rises again, and various aspects must be considered. This review explores the quest for new materials for successfully routing nanoscale interconnects. The challenges in the interconnect structures as physical dimensions shrink are first explored. Then, various problem-solving options are considered based on the properties of materials. New materials are also introduced for barriers, such as 2D materials, self-assembled molecular layers, high-entropy alloys, and conductors, such as Co and Ru, intermetallic compounds, and MAX phases. The comprehensive discussion of each material includes state-of-the-art studies ranging from the characteristics of materials by theoretical calculation to process applications to the current interconnect structures. This review intends to provide a materials-based implementation strategy to bridge the gap between academia and industry.

Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning

In order to determine the polarizability and hyperpolarizability of a molecule, several key parameters need to be known, including the excitation energy of the ground and excited states, the transition dipole moment, and the difference of dipole moment between the ground and excited states. In this study, a machine-learning model was developed and trained to predict the molecular polarizability and second-order hyperpolarizability on a subset of QM9 data set. The density of states was employed as input to the model. The results demonstrated that the machine-learning model effectively estimated both polarizability and the order of magnitude of second-order hyperpolarizability. However, the model was unable to predict the dipole moment and first-order hyperpolarizability, suggesting limitations in its ability to predict the difference of dipole moment between the ground and excited states. The computational efficiency of machine-learning models compared to traditional quantum mechanical calculations enables the possibility of large-scale screening of molecules that satisfy specific requirements using existing databases. This work presents a potential solution for the efficient exploration and analysis of molecules on a larger scale.

On the non-bonding valence band and the electronic properties of poly(triazine imide), a graphitic carbon nitride

Graphitic carbon nitrides are covalently-bonded, layered, and crystalline semiconductors with high thermal and oxidative stability. These properties make graphitic carbon nitrides potentially useful in overcoming the limitations of 0D molecular and 1D polymer semiconductors. In this contribution, we study structural, vibrational, electronic and transport properties of nano-crystals of poly(triazine-imide) (PTI) derivatives with intercalated Li- and Br-ions and without intercalates. Intercalation-free poly(triazine-imide) (PTI-IF) is corrugated or AB stacked and partially exfoliated. We find that the lowest energy electronic transition in PTI is forbidden due to a non-bonding uppermost valence band and that its electroluminescence from the π-π* transition is quenched which severely limits their use as emission layer in electroluminescent devices. THz conductivity in nano-crystalline PTI is up to eight orders of magnitude higher than the macroscopic conductivity of PTI films. We find that the charge carrier density of PTI nano-crystals is among the highest of all known intrinsic semiconductors, however, macroscopic charge transport in films of PTI is limited by disorder at crystal-crystal interfaces. Future device applications of PTI will benefit most from single crystal devices that make use of electron transport in the lowest, π-like conduction band.

Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature

Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications are demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, the temperature issue is addressed by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism is unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behavior of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems.

Compact optical convolution processing unit based on multimode interference

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations. Although the convolution kernels are interrelated, ten-class classification of handwritten digits from the MNIST database is experimentally demonstrated. The linear scalability of the proposed design with respect to computational size translates into a solid potential for large-scale integration.

2D fin field-effect transistors integrated with epitaxial high-k gate oxide

Precise integration of two-dimensional (2D) semiconductors and high-dielectric-constant (k) gate oxides into three-dimensional (3D) vertical-architecture arrays holds promise for developing ultrascaled transistors1-5, but has proved challenging. Here we report the epitaxial synthesis of vertically aligned arrays of 2D fin-oxide heterostructures, a new class of 3D architecture in which high-mobility 2D semiconductor fin Bi2O2Se and single-crystal high-k gate oxide Bi2SeO5 are epitaxially integrated. These 2D fin-oxide epitaxial heterostructures have atomically flat interfaces and ultrathin fin thickness down to one unit cell (1.2 nm), achieving wafer-scale, site-specific and high-density growth of mono-oriented arrays. The as-fabricated 2D fin field-effect transistors (FinFETs) based on Bi2O2Se/Bi2SeO5 epitaxial heterostructures exhibit high electron mobility (μ) up to 270 cm2 V-1 s-1, ultralow off-state current (IOFF) down to about 1 pA μm-1, high on/off current ratios (ION/IOFF) up to 108 and high on-state current (ION) up to 830 μA μm-1 at 400-nm channel length, which meet the low-power specifications projected by the International Roadmap for Devices and Systems (IRDS)6. The 2D fin-oxide epitaxial heterostructures open up new avenues for the further extension of Moore's law.

A steep-switching impact ionization-based threshold switching field-effect transistor

A steep switching device with a low subthreshold swing (SS) that overcomes the fundamental Boltzmann limit (kT/q) is required to efficiently process a continuously increasing amount of data. Recently, two-dimensional material-based impact ionization transistors with various structures have been reported with the advantages of a low critical electric field and a unique quantum confinement effect. However, most of them cannot retain steep switching at room temperature, and device performance degradation issues caused by impact ionization-induced hot carriers have not been structurally addressed. In this study, we presented an impact-ionization-based threshold switching field-effect transistor (I²S-FET) fabricated with a serial connection of a MoS₂ FET and WSe₂ impact ionization-based threshold switch (I²S). We obtained repetitive operation with low SS (32.8 mV dec^-1) at room temperature, along with low dielectric injection efficiency (10^-6), through a structural design with separation of the conducting region, which determines on-state carrier transport, and the steep-switching region where the transition from off- to on-state occurs via impact ionization. Furthermore, compared to previously reported threshold-switching devices, our device demonstrated hysteresis-free switching characteristics. This study provides a promising approach for developing next-generation energy-efficient electronic devices and ultralow-power applications.

Leveraging quantum computing for dynamic analyses of logical networks in systems biology

The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.

Fundamental energy cost of finite-time parallelizable computing

The fundamental energy cost of irreversible computing is given by the Landauer bound of [Formula: see text]/bit, where k is the Boltzmann constant and T is the temperature in Kelvin. However, this limit is only achievable for infinite-time processes. We here determine the fundamental energy cost of finite-time parallelizable computing within the framework of nonequilibrium thermodynamics. We apply these results to quantify the energetic advantage of parallel computing over serial computing. We find that the energy cost per operation of a parallel computer can be kept close to the Landauer limit even for large problem sizes, whereas that of a serial computer fundamentally diverges. We analyze, in particular, the effects of different degrees of parallelization and amounts of overhead, as well as the influence of non-ideal electronic hardware. We further discuss their implications in the context of current technology. Our findings provide a physical basis for the design of energy-efficient computers.

Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices

While there is an abundance of research on neural networks that are "inspired" by the brain, few mimic the critical temporal compute features that allow the brain to efficiently perform complex computations. Even fewer methods emulate the heterogeneity of learning produced by biological neurons. Memory devices, such as memristors, are also investigated for their potential to implement neuronal functions in electronic hardware. However, memristors in computing architectures typically operate as non-volatile memories, either as storage or as the weights in a multiply-and-accumulate function that requires direct access to manipulate memristance via a costly learning algorithm. Hence, the integration of memristors into architectures as time-dependent computational units is studied, starting with the development of a compact and versatile mathematical model that is capable of emulating flux-linkage controlled analog (FLCA) memristors and their unique temporal characteristics. The proposed model, which is validated against experimental FLCA Li_xNbO₂ intercalation devices, is used to create memristive circuits that mimic neuronal behavior such as desensitization, paired-pulse facilitation, and spike-timing-dependent plasticity. The model is used to demonstrate building blocks of biomimetic learning via dynamical memristive circuits that implement biomimetic learning rules in a self-training neural network, with dynamical memristive weights that are capable of associative lifelong learning. Successful training of the dynamical memristive neural network to perform image classification of handwritten digits is shown, including lifelong learning by having the dynamical memristive network relearn different characters in succession. An analog computing architecture that learns to associate input-to-input correlations is also introduced, with examples demonstrating image classification and pattern recognition without convolution. The biomimetic functions shown in this paper result from fully ion-driven memristive circuits devoid of integrating capacitors and thus are instructive for exploiting the immense potential of memristive technology for neuromorphic computation in hardware and allowing a common architecture to be applied to a wide range of learning rules, including STDP, magnitude, frequency, and pulse shape among others, to enable an inorganic implementation of the complex heterogeneity of biological neural systems.

LambdaPP: Fast and accessible protein-specific phenotype predictions

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.

The AI gambit: leveraging artificial intelligence to combat climate change-opportunities, challenges, and recommendations

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI's greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

Metal-Organic Frameworks-Based Memristors: Materials, Devices, and Applications

Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor. The selection of resistive switching layer materials is extremely important for fabricating of high performance memristors. As an organic-inorganic hybrid material, metal-organic frameworks (MOFs) have the advantages of both inorganic and organic materials, which makes the memristors using it as a resistive switching layer show the characteristics of fast erasing speed, outstanding cycling stability, conspicuous mechanical flexibility, good biocompatibility, etc. Herein, the recent advances of MOFs-based memristors in materials, devices, and applications are summarized, especially the potential applications of MOFs-based memristors in data storage and neuromorphic computing. There also are discussions and analyses of the challenges of the current research to provide valuable insights for the development of MOFs-based memristors.

Optical computing powers graph neural networks

Graph-based neural networks have promising perspectives but are limited by electronic bottlenecks. Our work explores the advantages of optical neural networks in the graph domain. We propose an optical graph neural network (OGNN) based on inverse-designed optical processing units (OPUs) to classify graphs with optics. The OPUs, combined with two types of optical components, can perform multiply-accumulate, matrix-vector multiplication, and matrix-matrix multiplication operations. The proposed OGNN can classify typical non-Euclidean MiniGCDataset graphs and successfully predict 1000 test graphs with 100% accuracy. The OPU-formed optical-electrical graph attention network is also scalable to handle more complex graph data, such as the Cora dataset, with 89.0% accuracy.

2D Materials-Based Static Random-Access Memory

2D transition-metal dichalcogenide semiconductors, such as MoS₂ and WSe₂ , with adequate bandgaps are promising channel materials for ultrascaled logic transistors. This scalability study of 2D material (2DM)-based field-effect transistor (FET) and static random-access memory (SRAM) cells analyzing the impact of layer thickness reveals that the monolayer 2DM FET with superior electrostatics is beneficial for its ability to mitigate the read-write conflict in an SRAM cell at scaled technology nodes (1-2.1 nm). Moreover, the monolayer 2DM SRAM exhibits lower cell read access time and write time than the bilayer and trilayer 2DM SRAM cells at fixed leakage power. This simulation predicts that the optimization of 2DM SRAM designed with state-of-the-art contact resistance, mobility, and equivalent oxide thickness leads to excellent stability and operation speed at the 1-nm node. Applying the nanosheet (NS) gate-all-around (GAA) structure to 2DM further reduces cell read access time and write time and improves the area density of the SRAM cells, demonstrating a feasible scaling path beyond Si technology using 2DM NSFETs. In addition to the device design, the process challenges for 2DM NSFETs, including the cost-effective stacking of 2DM layers, formation of electrical contacts, suspended 2DM channels, and GAA structures, are also discussed.

Engineering high quality graphene superlattices via ion milled ultra-thin etching masks

Nanofabrication research pursues the miniaturization of patterned feature size. In the current state of the art, micron scale areas can be patterned with features down to ~30 nm pitch using electron beam lithography. Here, we demonstrate a nanofabrication technique which allows patterning periodic structures with a pitch down to 16 nm. It is based on focused ion beam milling of suspended membranes, with minimal proximity effects typical to standard electron beam lithography. The membranes are then transferred and used as hard etching masks. We benchmark our technique by electrostatically inducing a superlattice potential in graphene and observe bandstructure modification in electronic transport. Our technique opens the path towards the realization of very short period superlattices in 2D materials, but with the ability to control lattice symmetries and strength. This can pave the way for a versatile solid-state quantum simulator platform and the study of correlated electron phases.

Van der Waals-Interface-Dominated All-2D Electronics

The interface is the device. As the feature size rapidly shrinks, silicon-based electronic devices are facing multiple challenges of material performance decrease and interface quality degradation. Ultrathin 2D materials are considered as potential candidates in future electronics by their atomically flat surfaces and excellent immunity to short-channel effects. Moreover, due to naturally terminated surfaces and weak van der Waals (vdW) interactions between layers, 2D materials can be freely stacked without the lattice matching limit to form high-quality heterostructure interfaces with arbitrary components and twist angles. Controlled interlayer band alignment and optimized interfacial carrier behavior allow all-2D electronics based on 2D vdW interfaces to exhibit more comprehensive functionality and better performance. Especially, achieving the same computing capacity of multiple conventional devices with small footprint all-2D devices is considered to be the key development direction of future electronics. Herein, the unique properties of all-2D vdW interfaces and their construction methods are systematically reviewed and the main performance contributions of different vdW interfaces in 2D electronics are summarized, respectively. Finally, the recent progress and challenges for all-2D vdW electronics are discussed, and how to improve the compatibility of 2D material devices with silicon-based industrial technology is pointed out as a critical challenge.

A steep switching WSe2 impact ionization field-effect transistor

The Fermi-Dirac distribution of carriers and the drift-diffusion mode of transport represent two fundamental barriers towards the reduction of the subthreshold slope (SS) and the optimization of the energy consumption of field-effect transistors. In this study, we report the realization of steep-slope impact ionization field-effect transistors (I²FETs) based on a gate-controlled homogeneous WSe₂ lateral junction. The devices showed average SS down to 2.73 mV/dec over three decades of source-drain current and an on/off ratio of ~10⁶ at room temperature and low bias voltages (<1 V). We determined that the lucky-drift mechanism of carriers is valid in WSe₂, allowing our I²FETs to have high impact ionization coefficients and low SS at room temperature. Moreover, we fabricated a logic inverter based on a WSe₂ I²FET and a MoS₂ FET, exhibiting an inverter gain of 73 and almost ideal noise margin for high- and low-logic states. Our results provide a promising approach for developing functional devices as front runners for energy-efficient electronic device technology.

The potential energy efficiency of impact ionization field-effect transistors (I²FETs) is usually limited by stringent operational conditions. Here, the authors report I²FETs based on 2D WSe₂, showing average subthreshold slopes down to 2.3 mV/dec and on/off ratios of ~10⁶ at room temperature and bias voltages <1 V.

Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at the device level to enable novel compute-in-memory (CIM) operations. A key challenge in the construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search, and neural network operations on sub-50 nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, nonvolatility, and nonlinearity of FeDs, search operations are demonstrated with a cell footprint <0.12 μm² when projected onto 45 nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms.

Multivalued Logic for Optical Computing with Photonically Enabled Chiral Bio-organic Structures

Photonic bio-organic multiphase structures are suggested here for integrated thin-film electronic nets with multilevel logic elements for multilevel computing via a reconfigurable photonic bandgap of chiral biomaterials. Herein, inspired by an artificial intelligence system with efficient information integration and computing capability, the photonically active dielectric layer of chiral nematic cellulose nanocrystals is combined with printed-in p- and n-type organic semiconductors as a bifunctional logical element. These adaptive logic elements are capable of triggering tailored quantized electrical output signals under light with different photon energy and at the different photonic bandgaps of the active dielectric layer. The bifunctional structures enable complex memory behavior upon repetitive changes of photonic bandgap (controlled by expansion/contraction of chiral nematic pitch) and photon energy (controlled by light absorption wavelength of complementary organic semiconductor layers), exhibiting effectively a reconfigurable ternary logic response. This proof-of-concept bio-assisted multivalued logic structure facilitates an optical computing system for low-power optical information processing integrated with human-machine interfaces.

Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing

Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO₃ doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10⁴), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO₃ is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.

Rotaxane nanomachines in future molecular electronics

As the electronics industry is integrating more and more new molecules to utilize them in logic circuits and memories to achieve ultra-high efficiency and device density, many organic structures emerged as promising candidates either in conjunction with or as an alternative to conventional semiconducting materials such as but not limited to silicon. Owing to rotaxane's mechanically interlocked molecular structure consisting of a dumbbell-shaped molecule threaded through a macrocycle, they could be excellent nanomachines in molecular switches and memory applications. As a nanomachine, the macrocycle of rotaxane can move reversibly between two stations along its axis under external stimuli, resulting in two stable molecular configurations known as "ON" and "OFF" states of the controllable switch with distinct resistance. There are excellent reports on rotaxane's structure, properties, and function relationship and its application to molecular electronics (Ogino, et al., 1984; Wu, et al., 1991; Bissell, et al., 1994; Collier, et al., 1999; Pease, et al., 2001; Chen, et al., 2003; Green, et al., 2007; Jia, et al., 2016). This comprehensive review summarizes [2]rotaxane and its application to molecular electronics. This review sorts the major research work into a multi-level pyramid structure and presents the challenges of [2]rotaxane's application to molecular electronics at three levels in developing molecular circuits and systems. First, we investigate [2]rotaxane's electrical characteristics with different driving methods and discuss the design considerations and roles based on voltage-driven [2]rotaxane switches that promise the best performance and compatibility with existing solid-state circuits. Second, we examine the solutions for integrating [2]rotaxane molecules into circuits and the limitations learned from these devices keep [2]rotaxane active as a molecular switch. Finally, applying a sandwiched crossbar structure and architecture to [2]rotaxane circuits reduces the fabrication difficulty and extends the possibility of reprogrammable [2]rotaxane arrays, especially at a system level, which eventually promotes the further realization of [2]rotaxane circuits.

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.

Insect-inspired AI for autonomous robots

Autonomous robots are expected to perform a wide range of sophisticated tasks in complex, unknown environments. However, available onboard computing capabilities and algorithms represent a considerable obstacle to reaching higher levels of autonomy, especially as robots get smaller and the end of Moore's law approaches. Here, we argue that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence (AI) needed for the autonomy of small, mobile robots. The advantage of insect intelligence stems from its resource efficiency (or parsimony) especially in terms of power and mass. First, we discuss the main aspects of insect intelligence underlying this parsimony: embodiment, sensory-motor coordination, and swarming. Then, we take stock of where insect-inspired AI stands as an alternative to other approaches to important robotic tasks such as navigation and identify open challenges on the road to its more widespread adoption. Last, we reflect on the types of processors that are suitable for implementing insect-inspired AI, from more traditional ones such as microcontrollers and field-programmable gate arrays to unconventional neuromorphic processors. We argue that even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.

Gate-Defined Quantum Confinement in CVD 2D WS2

Temperature-dependent transport measurements are performed on the same set of chemical vapor deposition (CVD)-grown WS₂ single- and bilayer devices before and after atomic layer deposition (ALD) of HfO₂ . This isolates the influence of HfO₂ deposition on low-temperature carrier transport and shows that carrier mobility is not charge impurity limited as commonly thought, but due to another important but commonly overlooked factor: interface roughness. This finding is corroborated by circular dichroic photoluminescence spectroscopy, X-ray photoemission spectroscopy, cross-sectional scanning transmission electron microscopy, carrier-transport modeling, and density functional modeling. Finally, electrostatic gate-defined quantum confinement is demonstrated using a scalable approach of large-area CVD-grown bilayer WS₂ and ALD-grown HfO₂ . The high dielectric constant and low leakage current enabled by HfO₂ allows an estimated quantum dot size as small as 58 nm. The ability to lithographically define increasingly smaller devices is especially important for transition metal dichalcogenides due to their large effective masses, and should pave the way toward their use in quantum information processing applications.

High-κ perovskite membranes as insulators for two-dimensional transistors

The scaling of silicon metal-oxide-semiconductor field-effect transistors has followed Moore's law for decades, but the physical thinning of silicon at sub-ten-nanometre technology nodes introduces issues such as leakage currents¹. Two-dimensional (2D) layered semiconductors, with an atomic thickness that allows superior gate-field penetration, are of interest as channel materials for future transistors^2,3. However, the integration of high-dielectric-constant (κ) materials with 2D materials, while scaling their capacitance equivalent thickness (CET), has proved challenging. Here we explore transferrable ultrahigh-κ single-crystalline perovskite strontium-titanium-oxide membranes as a gate dielectric for 2D field-effect transistors. Our perovskite membranes exhibit a desirable sub-one-nanometre CET with a low leakage current (less than 10^-2 amperes per square centimetre at 2.5 megavolts per centimetre). We find that the van der Waals gap between strontium-titanium-oxide dielectrics and 2D semiconductors mitigates the unfavourable fringing-induced barrier-lowering effect resulting from the use of ultrahigh-κ dielectrics⁴. Typical short-channel transistors made of scalable molybdenum-disulfide films by chemical vapour deposition and strontium-titanium-oxide dielectrics exhibit steep subthreshold swings down to about 70 millivolts per decade and on/off current ratios up to 10⁷, which matches the low-power specifications suggested by the latest International Roadmap for Devices and Systems⁵.