Two-dimensional materials for next-generation computing technologies

Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning

Triggered by the pioneering research on graphene, the family of two-dimensional layered materials (2DLMs) has been investigated for more than a decade, and appealing functionalities have been demonstrated. However, there are still challenges inhibiting high-quality growth and circuit-level integration, and results from previous studies are still far from complying with industrial standards. Here, we overcome these challenges by utilizing machine-learning (ML) algorithms to evaluate key process parameters that impact the electrical characteristics of MoS₂ top-gated field-effect transistors (FETs). The wafer-scale fabrication processes are then guided by ML combined with grid searching to co-optimize device performance, including mobility, threshold voltage and subthreshold swing. A 62-level SPICE modeling was implemented for MoS₂ FETs and further used to construct functional digital, analog, and photodetection circuits. Finally, we present wafer-scale test FET arrays and a 4-bit full adder employing industry-standard design flows and processes. Taken together, these results experimentally validate the application potential of ML-assisted fabrication optimization for beyond-silicon electronic materials.

Here, the authors demonstrate the application of machine learning to optimize the device fabrication process for wafer-scale 2D semiconductors, and eventually fabricate digital, analog, and optoelectrical circuits.

Homogeneous neuromorphic hardware

[Figure: see text].

2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware

[Figure: see text].

Ultrathin Multibridge Channel Transistor Enabled by van der Waals Assembly

Multibridge channel field-effect transistors (MBCFETs) enable improved gate control and flow of a large drive current and they are regarded as promising candidates for next-generation transistor architecture. However, in achieving a larger drive current with a thinner channel, limitations arise from the decrease in mobility when the thickness of the Si nanosheet is less than 5 nm. In addition, an increase in the leakage current is unavoidable when a large number of channels are stacked. Here, a 2D ultrathin MBCFET is demonstrate, constructed based on 2 nm/2 nm MoS₂ channels. The normalized drive current (23.11 µA*µm µm^-1 ) in each level channel of this MBCFET exceeds that of the latest seven-level-stacked Si MBCFET, while the leakage current is only 0.4% of this value, with the subthreshold swing reaching 60 mV dec^-1 and an on/off ratio reaching up to 4 × 10⁸ at room temperature. Furthermore, the drive current of this 2D ultrathin MBCFET can be further increased by regulating the polarity of the operation voltage to reduce the injection barrier. The combination of 2D materials and an MBC structure has the potential for use in high-performance and low-power-consumption electronics.

Realization of a non-markov chain in a single 2D mineral RRAM

The non-Markov process exists widely in thermodymanic process, while it usually requires the packing of many transistors and memories with great system complexity in a traditional device structure to minic such functions. Two-dimensional (2D) material-based resistive random access memory (RRAM) devices have the potential for next-generation computing systems with much-reduced complexity. Here, we achieve a non-Markov chain in an individual RRAM device based on 2D mineral material mica with a vertical metal/mica/metal structure. We find that the potassium ions (K⁺) in 2D mica gradually move in the direction of the applied electric field, making the initially insulating mica conductive. The accumulation of K⁺ is changed by an electric field, and the 2D-mica RRAM has both single and double memory windows, a high on/off ratio, decent stability, and repeatability. This is the first time a non-Markov chain process has been established in a single RRAM, in which the movement of K⁺ is dependent on the stimulated voltage as well as their past states. This work not only uncovers an intrinsic inner ionic conductivity of 2D mica, but also opens the door for the production of such RRAM devices with numerous functions and applications.

Light-Programmable Logic-in-Memory in 2D Semiconductors Enabled by Supramolecular Functionalization: Photoresponsive Collective Effect of Aligned Molecular Dipoles

Nowadays, the unrelenting growth of the digital universe calls for radically novel strategies for data processing and storage. An extremely promising and powerful approach relies on the development of logic-in-memory (LiM) devices through the use of floating gate and ferroelectric technologies to write and erase data in a memory operating as a logic gate driven by electrical bias. In this work, we report an alternative approach to realize the logic-in-memory based on two-dimensional (2D) transition metal dichalcogenides (TMDs) where multiple memorized logic output states have been established via the interface with responsive molecular dipoles arranged in supramolecular arrays. The collective dynamic molecular dipole changes of the axial ligand coordinated onto self-assembled metal phthalocyanine nanostructures on the surface of 2D TMD enables large reversible modulation of the Fermi level of both n-type molybdenum disulfide (MoS₂) and p-type tungsten diselenide (WSe₂) field-effect transistors (FETs), to achieve multiple memory states by programming and erasing with ultraviolet (UV) and with visible light, respectively. As a result, logic-in-memory devices were built up with our supramolecular layer/2D TMD architecture where the output logic is encoded by the motion of the molecular dipoles. Our strategy relying on the dynamic control of the 2D electronics by harnessing the functions of molecular-dipole-induced memory in a supramolecular hybrid layer represents a versatile way to integrate the functional programmability of molecular science into the next generation nanoelectronics.

Low-Temperature Co-hydroxylated Cu/SiO2 Hybrid Bonding Strategy for a Memory-Centric Chip Architecture

Cu/SiO₂ hybrid bonding with planarized dielectric and isolated metal connections can realize ultradense interconnects (e.g., ≤1 μm) by eliminating the microbumps and underfill through the direct bonding of Cu-Cu and SiO₂-SiO₂. However, the low-temperature bonding of Cu-Cu (oxide-free surface) and SiO₂-SiO₂ (hydroxylated surface) is difficult to be compatible in a mechanism. We circumvent this contradiction by constructing a co-hydroxylated functional surface on a Cu/SiO₂ hybrid platform. By combining and optimizing the protocol of Ar/O₂ plasma activation and formic acid solution immersion, an -OH active layer was successfully established on the Cu and SiO₂ surfaces simultaneously, and the increased total surface area provided more adsorption sites for hydroxyl groups. A Cu-Cu interface with sufficient atom diffusion, substantial grain growth, and fewer microvoids was obtained at 200 °C. Notably, the carbon-related interlayer that may degrade the interfacial performance could be effectively inhibited across the optimized SiO₂-SiO₂ interface even if organic acid was introduced in the protocol. This low-temperature Cu/SiO₂ hybrid bonding via a co-hydroxylated strategy may inspire the development of a memory-centric chip architecture and functional integrated circuits delivering a monolithic-like performance in the future hyperscaling era.

Ultrafast non-volatile flash memory based on van der Waals heterostructures

Flash memory has become a ubiquitous solid-state memory device widely used in portable digital devices, computers and enterprise applications. The development of the information age has demanded improvements in memory speed and retention performance. Here we demonstrate an ultrafast non-volatile flash memory based on MoS₂/hBN/multilayer graphene van der Waals heterostructures, which achieves an ultrafast writing/erasing speed of 20 ns through two-triangle-barrier modified Fowler-Nordheim tunnelling. Using detailed theoretical analysis and experimental verification, we postulate that a suitable barrier height, gate coupling ratio and clean interface are the main reasons for the breakthrough writing/erasing speed of our flash memory devices. Because of its non-volatility this ultrafast flash memory could provide the foundation for the next generation of high-speed non-volatile memory.

The Photodetectors Based on Lateral Monolayer MoS2/WS2 Heterojunctions

Monolayer transition metal dichalcogenides (TMDs) show promising potential for next-generation optoelectronics due to excellent light capturing and photodetection capabilities. Photodetectors, as important components of sensing, imaging and communication systems, are able to perceive and convert optical signals to electrical signals. Herein, the large-area and high-quality lateral monolayer MoS2/WS2 heterojunctions were synthesized via the one-step liquid-phase chemical vapor deposition approach. Systematic characterization measurements have verified good uniformity and sharp interfaces of the channel materials. As a result, the photodetectors enhanced by the photogating effect can deliver competitive performance, including responsivity of ~ 567.6 A/W and detectivity of ~ 7.17 × 1011 Jones. In addition, the 1/f noise obtained from the current power spectrum is not conductive to the development of photodetectors, which is considered as originating from charge carrier trapping/detrapping. Therefore, this work may contribute to efficient optoelectronic devices based on lateral monolayer TMD heterostructures.

Self-Powered Filterless On-Chip Full-Stokes Polarimeter

The detection of polarization states of light is essential in photonic and optoelectronic devices. Currently, the polarimeters are usually constructed with the help of waveplates or a comprehensive metasurface, which will inevitably increase the fabrication complexity and unnecessary energy loss. Here, we have successfully demonstrated a self-powered filterless on-chip full-Stokes polarimeter based on a single-layer MoS₂/few-layer MoS₂ homojunction. Combining the built-in electric field enhanced circular photogalvanic effect with the intrinsic optical anisotropy of MoS₂ between in-plane and out-of-plane directions, the device is able to conveniently sense four Stokes parameters of incident light at zero bias without requiring an extra filtering layer and can function in the wavelength range of 650-690 nm with acceptable average errors. Besides, this homojunction device is easy to integrate with silicon-based chips and could have much smaller sizes than metasurface based polarimeters. Our study thus provides an excellent paradigm for high-performance on-chip filterless polarimeters.

Memristor Based on Inorganic and Organic Two-Dimensional Materials: Mechanisms, Performance, and Synaptic Applications

A memristor is a two-terminal device with nonvolatile resistive switching (RS) behaviors. Recently, memristors have been highly desirable for both fundamental research and technological applications because of their great potential in the development of high-density memory technology and neuromorphic computing. Benefiting from the unique two-dimensional (2D) layered structure and outstanding properties, 2D materials have proven to be good candidates for use in gate-tunable, highly reliable, heterojunction-compatible, and low-power memristive devices. More intriguing, stable and reliable nonvolatile RS behaviors can be achieved in multi- and even monolayer 2D materials, which seems unlikely to be achieved in traditional oxides with thicknesses less than a few nanometers because of the leakage currents. Moreover, such two-terminal devices show a series of synaptic functionalities, suggesting applications in simulating a biological synapse in the neural network. In this review article, we summarize the recent progress in memristors based on inorganic and organic 2D materials, from the material synthesis, device structure and fabrication, and physical mechanism to some versatile memristors based on diverse 2D materials with good RS properties and memristor-based synaptic applications. The development prospects and challenges at the current stage are then highlighted, which is expected to inspire further advancements and new insights into the fields of information storage and neuromorphic computing.

Controlled Doping Engineering in 2D MoS2 Crystals toward Performance Augmentation of Optoelectronic Devices

Doping engineering of two-dimensional (2D) semiconductors is vital for expanding their device applications, but has been limited by the inhomogeneous distribution of doping atoms in such an ultrathin thickness. Here, we report the controlled doping of Sn heteroatoms into 2D MoS₂ crystals through a single-step deposition method to improve the photodetection ability of MoS₂ flakes, whereas the host lattice has been well reserved without the random aggregation of the introduced atoms. Atomic-resolution and spectroscopic characterizations provide direct evidence that Sn atoms have been substitutionally doped at Mo sites in the MoS₂ lattice and the Sn dopant leads to an additional strain in the host lattice. The detection performance of Sn-doped MoS₂ flakes exhibits an order of magnitude improvement (up to R_λ ≈ 29 A/W, EQE ≈ 7.8 × 10³%, D* ≈ 10¹¹ Jones@470 nm) as compared with that of pure MoS₂ flakes, which is associated with electrons released from Sn atoms. Such a substitutional doping process in TMDs provides a potential platform to tune the on-demand properties of these 2D materials.

Electronic and thermal transport in novel carbon-based bilayer with tetragonal rings: a combined study using first-principles and machine learning approach

In this article, the structural, electronic and thermal transport characteristics of bilayer tetragonal graphene (TG) are systematically explored with a combination of first-principles calculations and machine-learning interatomic potential approaches. Optimized ground state geometry of the bilayer TG structure is predicted and examined by employing various stability criteria. Electronic bandstructure analysis confirmed that bilayer TG exhibits a metallic band structure similar to the monolayer T-graphene structure. Thermal transport characteristics of the bilayer TG structure are explored by analysing thermal conductivity, the Seebeck coefficient, and electrical conductivity. The electronic part of the thermal conductivity shows linearly increasing behaviour with temperature, however the lattice part exhibits the opposite character. The lattice thermal conductivity part is investigated in terms of the three phonon scattering rates and weighted phase space. On the other hand, the Seebeck coefficient goes through a transition from negative to positive values with increasing temperature. The Wiedemann-Franz law regarding electrical transport of the bilayer TG is verified and confirms the universal Lorentz number. Specific heat of the bilayer TG structure follows the Debye model at low temperature and constant behaviour at high temperature. Moreover, the Debye temperature of the bilayer TG structure is verified by ab initio calculations as well as fitting the specific heat data using the Debye model.

Emerging 2D Memory Devices for In-Memory Computing

It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonvolatile memory unit, has been intensively studied. Among various candidate materials, 2D layered materials have recently demonstrated many new features that have been uniquely exploited to build next-generation electronics. Here, the recent progress of 2D memory devices is reviewed for in-memory computing. For each memory configuration, their operation mechanisms and memory characteristics are described, and their pros and cons are weighed. Subsequently, their versatile applications for in-memory computing technology, including logic operations, electronic synapses, and random number generation are presented. Finally, the current challenges and potential strategies for future 2D in-memory computing systems are also discussed at the material, device, circuit, and architecture levels. It is hoped that this manuscript could give a comprehensive review of 2D memory devices and their applications in in-memory computing, and be helpful for this exciting research area.

Double-Gate MoS2 Field-Effect Transistors with Full-Range Tunable Threshold Voltage for Multifunctional Logic Circuits

Multifunctional reconfigurable devices, with higher information capacity, smaller size, and more functions, are urgently needed and draw most attention in frontiers in information technology. 2D semiconductors, ascribing to ultrathin body and easy electrostatic control, show great potential in developing reconfigurable functional units. This work proposes a novel double-gate field-effect transistor architecture with equal top and bottom gate (TG and BG) and realizes flexible optimization of the subthreshold swing (SS) and threshold voltage (V_TH ). While the TG and BG are used simultaneously, as a single gate to drive the transistor, ultralow average SS value of 65.5 mV dec^-1 can be obtained in a large current range over 10⁴ , enabling the application in high gain inverter. While one gate is used to initialize the channel doping, full logic swing inverter circuit with high noise margin (over 90%) is demonstrated. Such device prototype is further extended for designing reconfigurable logic applications and can be dynamically switched and well maintained between binary and ternary logics. This study provides important concept and device prototype for future multifunctional logic applications.

An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations

In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in a small area with high energy efficiency remains a challenge. In this work, we propose a circuit architecture that integrates monolayer MoS₂ transistors in a two-transistor–one-capacitor (2T-1C) configuration. In this structure, the memory portion is similar to a 1T-1C Dynamic Random Access Memory (DRAM) so that theoretically the cycling endurance and erase/write speed inherit the merits of DRAM. Besides, the ultralow leakage current of the MoS₂ transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The electrical characteristics of a single MoS₂ transistor also allow analog computation by multiplying the drain voltage by the stored voltage on the capacitor. The sum-of-product is then obtained by converging the currents from multiple 2T-1C units. Based on our experiment results, a neural network is ex-situ trained for image recognition with 90.3% accuracy. In the future, such 2T-1C units can potentially be integrated into three-dimensional (3D) circuits with dense logic and memory layers for low power in-situ training of neural networks in hardware.

In standard computing architectures, memory and logic circuits are separated, a feature that slows matrix operations vital to deep learning algorithms. Here, the authors present an alternate in-memory architecture and demonstrate a feasible approach for analog matrix multiplication.

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

Energy-efficient transistors: suppressing the subthreshold swing below the physical limit

With the miniaturization of silicon-based electronic components, power consumption is becoming a fundamental issue for micro-nano electronic circuits. The main reason for this is that the scaling of the supply voltage in the ultra-large-scale integrated circuit cannot keep up with the shrinking of the characteristic size of conventional transistors due to the physical limit termed "Boltzmann Tyranny", in which a gate voltage of at least 60 mV is required to modulate the drain current by one order of magnitude. Accordingly, to solve this problem, several new transistor architectures have been designed to reduce the subthreshold swing (SS) to lower than the fundamental limitation, thus lowering the supply voltage and reducing the power consumption. In this review, we first analytically formulate the SS, summarize the methods for reducing the SS, and propose four new transistor concepts, including tunnelling field-effect transistor, negative capacitance field-effect transistor, impact ionization field-effect transistor, and cold source field-effect transistor. Then, we review their physical mechanisms and optimization methods and consider the potential and drawbacks of these four new transistors. Finally, we discuss the challenges encountered in the investigation of these steep-slope transistors and present the future outlook.

Investigation of charge trapping mechanism in MoS2field effect transistor by incorporating Al into host La2O3as gate dielectric

The charge trapping effect plays a key role in multi-bit memory devices and brain-like neuron devices. Herein, MoS₂field effect transistors are fabricated, incorporating Al into host La₂O₃as the gate dielectric, which exhibit excellent electrical properties with an on-off ratio in the memory window of ∼10⁶and a memory window ratio of ∼40%. Furthermore, the charge trapping and de-trapping processes were systematically studied, and the time constants are obtained from time-domain characteristics. Making use of the charge trapping effect, the threshold voltage of the device can be continuously adjusted. The oxide layer trap density and the interface state trap density are extracted using the charge separation method. These theoretical studies provide a deeper understanding of ways to control the charge trapping process, benefitting the commercialization of two-dimensional electronic devices and the development of new charge trapping devices.

In-sensor reservoir computing for language learning via two-dimensional memristors

The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.

Dielectric Engineered Two-Dimensional Neuromorphic Transistors

Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable synaptic arrays with complementary metal-oxide-semiconductor compatibility remains challenging. Here, we report a 2D transition metal dichalcogenide-based synaptic array fabricated on commercial silicon-rich silicon nitride (sr-SiN_x) substrate. The array demonstrates uniform performance with sufficiently high analogue on/off ratio and linear conductance update, and low cycle-to-cycle variability (1.5%) and device-to-device variability (5.3%), which are essential for neuromorphic hardware implementation. On the basis of the experimental data, we further prove that the artificial synapses can achieve a recognition accuracy of 91% on the MNIST handwritten data set. Our findings offer a simple approach to achieve 2D synaptic arrays by using an industry-compatible sr-SiN_x dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.

Material and Device Structure Designs for 2D Memory Devices Based on the Floating Gate Voltage Trajectory

Two-dimensional heterostructures have been extensively investigated as next-generation nonvolatile memory (NVM) devices. In the past decade, drastic performance improvements and further advanced functionalities have been demonstrated. However, this progress is not sufficiently supported by the understanding of their operations, obscuring the material and device structure design policy. Here, detailed operation mechanisms are elucidated by exploiting the floating gate (FG) voltage measurements. Systematic comparisons of MoTe₂, WSe₂, and MoS₂ channel devices revealed that the tunneling behavior between the channel and FG is controlled by three kinds of current-limiting paths, i.e., tunneling barrier, 2D/metal contact, and p-n junction in the channel. Furthermore, the control experiment indicated that the access region in the device structure is required to achieve 2D channel/FG tunneling by preventing electrode/FG tunneling. The present understanding suggests that the ambipolar 2D-based FG-type NVM device with the access region is suitable for further realizing potentially high electrical reliability.

Seeded 2D epitaxy of large-area single-crystal films of the van der Waals semiconductor 2H MoTe2

The integration of two-dimensional (2D) van der Waals semiconductors into silicon electronics technology will require the production of large-scale, uniform, and highly crystalline films. We report a route for synthesizing wafer-scale single-crystalline 2H molybdenum ditelluride (MoTe₂) semiconductors on an amorphous insulating substrate. In-plane 2D-epitaxy growth by tellurizing was triggered from a deliberately implanted single seed crystal. The resulting single-crystalline film completely covered a 2.5-centimeter wafer with excellent uniformity. The 2H MoTe₂ 2D single-crystalline film can use itself as a template for further rapid epitaxy in a vertical manner. Transistor arrays fabricated with the as-prepared 2H MoTe₂ single crystals exhibited high electrical performance, with excellent uniformity and 100% device yield.

Fundamental limit of microresonator field uniformity and slow light enabled ultraprecise displacement metrology

We determine the fundamental limit of microresonator field uniformity. It can be achieved in a specially designed microresonator, called a bat microresonator, fabricated at the optical fiber surface. We show that the relative nonuniformity of an eigenmode amplitude along the axial length L of an ideal bat microresonator cannot be smaller than 13π²n⁴λ^-4Q^-2L⁴, where n,λ, and Q are its refractive index, eigenmode wavelength, and Q-factor, respectively. For a silica microresonator with Q=10⁸, this eigenmode has axial speed ∼10^-4c, where c is the speed of light in vacuum, and its nonuniformity along length L=100\unicode{x00B5}m at wavelength λ=1.5µm is ∼10^-7. For a realistic fiber with diameter 100 µm and surface roughness 0.2 nm, the smallest eigenmode nonuniformity is ∼0.0003. As an application, we consider a bat microresonator evanescently coupled to high Q-factor silica microspheres, which serves as a reference supporting ultraprecise straight-line translation.

Memristive Artificial Synapses for Neuromorphic Computing

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Interface chemistry of two-dimensional heterostructures - fundamentals to applications

Two-dimensional heterostructures (2D HSs) have emerged as a new class of materials where dissimilar 2D materials are combined to synergise their advantages and alleviate shortcomings. Such a combination of dissimilar components into 2D HSs offers fascinating properties and intriguing functionalities attributed to the newly formed heterointerface of constituent components. Understanding the nature of the surface and the complex heterointerface of HSs at the atomic level is crucial for realising the desired properties, designing innovative 2D HSs, and ultimately unlocking their full potential for practical applications. Therefore, this review provides the recent progress in the field of 2D HSs with a focus on the discussion of the fundamentals and the chemistry of heterointerfaces based on van der Waals (vdW) and covalent interactions. It also explains the challenges associated with the scalable synthesis and introduces possible methodologies to produce large quantities with good control over the heterointerface. Subsequently, it highlights the specialised characterisation techniques to reveal the heterointerface formation, chemistry and nature. Afterwards, we give an overview of the role of 2D HSs in various emerging applications, particularly in high-power batteries, bifunctional catalysts, electronics, and sensors. In the end, we present conclusions with the possible solutions to the associated challenges with the heterointerfaces and potential opportunities that can be adopted for innovative applications.

Defect Engineering of Two-Dimensional Transition-Metal Dichalcogenides: Applications, Challenges, and Opportunities

Atomic defects, being the most prevalent zero-dimensional topological defects, are ubiquitous in a wide range of 2D transition-metal dichalcogenides (TMDs). They could be intrinsic, formed during the initial sample growth, or created by postprocessing. Despite the majority of TMDs being largely unaffected after losing chalcogen atoms in the outermost layer, a spectrum of properties, including optical, electrical, and chemical properties, can be significantly modulated, and potentially invoke applicable functionalities utilized in many applications. Hence, controlling chalcogen atomic defects provides an alternative avenue for engineering a wide range of physical and chemical properties of 2D TMDs. In this article, we review recent progress on the role of chalcogen atomic defects in engineering 2D TMDs, with a particular focus on device performance improvements. Various approaches for creating chalcogen atomic defects including nonstoichiometric synthesis and postgrowth treatment, together with their characterization and interpretation are systematically overviewed. The tailoring of optical, electrical, and magnetic properties, along with the device performance enhancement in electronic, optoelectronic, chemical sensing, biomedical, and catalytic activity are discussed in detail. Postformation dynamic evolution and repair of chalcogen atomic defects are also introduced. Finally, we offer our perspective on the challenges and opportunities in this field.

In Situ Resistive Switching Effect Scrutinization on Co-Designed Graphene Sensor

Resistive switching (RS), an electric property based on the forming and rupture of conductive filaments in metal-insulator-metal structures, has attracted intensive attention due to its potential application in next generation energy-efficient and area-efficient memory devices. In situ studies of the RS effect are urgently needed for its mechanism understanding and memristive performance improvement. Here investigations of both the RS effect as well as the gate tunable conductance quantization effect are realized by co-designing an Ag/SiO₂ based memory structure on a graphene local sensor. This design enables self-monitoring of the working states of the memristor in real-time by virtue of the graphene sensor. These findings pave the way for further investigations of on-chip electronics and quantum physics.

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs.

Spectroscopic Determination of Key Energy Scales for the Base Hamiltonian of Chromium Trihalides

The van der Waals (vdW) chromium trihalides (CrX₃) exhibit field-tunable, two-dimensional magnetic orders that vary with the halogen species and the number of layers. Their magnetic ground states with proximity in energies are sensitive to the degree of ligand-metal (p-d) hybridization and relevant modulations in the Cr d-orbital interactions. We use soft X-ray absorption (XAS) and resonant inelastic X-ray scattering (RIXS) spectroscopy at Cr L-edge along with the atomic multiplet simulations to determine the key energy scales such as the crystal field 10 Dq and interorbital Coulomb interactions under different ligand metal charge transfer (LMCT) in CrX₃ (X= Cl, Br, and I). Through this systematic study, we show that our approach compared to the literature has yielded a set of more reliably determined parameters for establishing a base Hamiltonian for CrX₃.

Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In₂Se₃ ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency.

Ambipolar 2D Semiconductors and Emerging Device Applications

With the rise of 2D materials, new physics and new processing techniques have emerged, triggering possibilities for the innovation of electronic and optoelectronic devices. Among them, ambipolar 2D semiconductors are of excellent gate-controlled capability and distinctive physical characteristic that the major charge carriers can be dynamically, reversibly and rapidly tuned between holes and electrons by electrostatic field. Based on such properties, novel devices, like ambipolar field-effect transistors, light-emitting transistors, electrostatic-field-charging PN diodes, are developed and show great advantages in logic and reconfigurable circuits, integrated optoelectronic circuits, and artificial neural network image sensors, enriching the functions of conventional devices and bringing breakthroughs to build new architectures. This review first focuses on the basic knowledge including fundamental principle of ambipolar semiconductors, basic material preparation techniques, and how to obtain the ambipolar behavior through electrical contact engineering. Then, the current ambipolar 2D semiconductors and their preparation approaches and main properties are summarized. Finally, the emerging new device structures are overviewed in detail, along with their novel electronic and optoelectronic applications. It is expected to shed light on the future development of ambipolar 2D semiconductors, exploring more new devices with novel functions and promoting the applications of 2D materials.

Field-effect at electrical contacts to two-dimensional materials

The inferior electrical contact to two-dimensional (2D) materials is a critical challenge for their application in post-silicon very large-scale integrated circuits. Electrical contacts were generally related to their resistive effect, quantified as contact resistance. With a systematic investigation, this work demonstrates a capacitive metal-insulator-semiconductor (MIS) field-effect at the electrical contacts to 2D materials: The field-effect depletes or accumulates charge carriers, redistributes the voltage potential, and gives rise to abnormal current saturation and nonlinearity. On one hand, the current saturation hinders the devices' driving ability, which can be eliminated with carefully engineered contact configurations. On the other hand, by introducing the nonlinearity to monolithic analog artificial neural network circuits, the circuits' perception ability can be significantly enhanced, as evidenced using a coronavirus disease 2019 (COVID-19) critical illness prediction model. This work provides a comprehension of the field-effect at the electrical contacts to 2D materials, which is fundamental to the design, simulation, and fabrication of electronics based on 2D materials.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material (results of the simulation and SEM) is available in the online version of this article at 10.1007/s12274-021-3670-y.

Growth and Interlayer Engineering of 2D Layered Semiconductors for Future Electronics

Layered materials that do not form a covalent bond in a vertical direction can be prepared in a few atoms to one atom thickness without dangling bonds. This distinctive characteristic of limiting thickness around the sub-nanometer level allowed scientists to explore various physical phenomena in the quantum realm. In addition to the contribution to fundamental science, various applications were proposed. Representatively, they were suggested as a promising material for future electronics. This is because (i) the dangling-bond-free nature inhibits surface scattering, thus carrier mobility can be maintained at sub-nanometer range; (ii) the ultrathin nature allows the short-channel effect to be overcome. In order to establish fundamental discoveries and utilize them in practical applications, appropriate preparation methods are required. On the other hand, adjusting properties to fit the desired application properly is another critical issue. Hence, in this review, we first describe the preparation method of layered materials. Proper growth techniques for target applications and the growth of emerging materials at the beginning stage will be extensively discussed. In addition, we suggest interlayer engineering via intercalation as a method for the development of artificial crystal. Since infinite combinations of the host-intercalant combination are possible, it is expected to expand the material system from the current compound system. Finally, inevitable factors that layered materials must face to be used as electronic applications will be introduced with possible solutions. Emerging electronic devices realized by layered materials are also discussed.

Recent Advances in Two-Dimensional Spintronics

Spintronics is the most promising technology to develop alternative multi-functional, high-speed, low-energy electronic devices. Due to their unusual physical characteristics, emerging two-dimensional (2D) materials provide a new platform for exploring novel spintronic devices. Recently, 2D spintronics has made great progress in both theoretical and experimental researches. Here, the progress of 2D spintronics has been reviewed. In the last, the current challenges and future opportunities have been pointed out in this field.