MoS2 Memtransistors Fabricated by Localized Helium Ion Beam Irradiation

Two-Dimensional Tunneling Memtransistor with Thin-Film Heterostructure for Low-Power Logic-in-Memory Complementary Metal-Oxide Semiconductor

With the demand for high-performance and miniaturized semiconductor devices continuously rising, the development of innovative tunneling transistors via efficient stacking methods using two-dimensional (2D) building blocks has paramount importance in the electronic industry. Hence, 2D semiconductors with atomically thin geometries hold significant promise for advancements in electronics. In this study, we introduced tunneling memtransistors with a thin-film heterostructure composed of 2D semiconducting MoS2 and WSe2. Devices with the dual function of tuning and memory operation were realized by the gate-regulated modulation of the barrier height at the heterojunction and manipulation of intrinsic defects within the exfoliated nanoflakes using solution processes. Further, our investigation revealed extensive edge defects and four distinct defect types, namely monoselenium vacancies, diselenium vacancies, tungsten vacancies, and tungsten adatoms, in the interior of electrochemically exfoliated WSe2 nanoflakes. Additionally, we constructed complementary metal-oxide semiconductor-based logic-in-memory devices with a small static power in the range of picowatts using the developed tunneling memtransistors, demonstrating a promising approach for next-generation low-power nanoelectronics.

Integration of MoS2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization

In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.

Applying the Wake-Up-like Effect to Enhance the Capabilities of Two-Dimensional Ferroelectric Field-Effect Transistors

For traditional ferroelectric field-effect transistors (FeFETs), enhancing the polarization domain of bulk ferroelectric materials is essential to improve device performance. However, there has been limited investigation into the enhancement of polarization field in two-dimensional (2D) ferroelectric material such as CuInP2S6 (CIPS). In this study, similar to bulk ferroelectric materials, CIPS exhibited enhanced polarization field upon application of external cyclic voltage. Moreover, unlike traditional ferroelectric materials, the polarization enhancement of CIPS is not due to redistribution of the defect but rather originates from a mechanism: the long-distance migration of Cu ions. We termed this mechanism the "wake-up-like effect". After incorporating the wake-up-like effect into the graphene/CIPS/WSe2 FeFET device, we successfully increased the hysteresis window and enhanced the current on/off ratio by 4 orders of magnitude. Moreover, the FeFET yielded remarkable achievements, such as multilevel nonvolatile memory with 21 distinct conductance levels, a high on/off ratio exceeding 106, a long retention time exceeding 103 s, and neuromorphic computing with 93% accuracy at recognizing handwritten digits. Introducing the wake-up-like effect to 2D CIPS may pave the way for innovative approaches to achieve advanced multilevel nonvolatile memory and neuromorphic computing capabilities for next-generation micro-nanoelectronic devices.

Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors

The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.

Submicron Memtransistors Made from Monocrystalline Molybdenum Disulfide

Multiterminal memtransistors made from two-dimensional (2D) materials have garnered increasing attention in the pursuit of low-power heterosynaptic neuromorphic circuits. However, existing 2D memtransistors tend to necessitate high set voltages (>1 V) or feature defective channels, posing concerns regarding material integrity and intrinsic properties. Herein, we present a monocrystalline monolayer MoS2 memtransistor designed for operation within submicron regimes. Under reverse drain bias sweeps, our experiments reveal memristive behavior within the device, further controllable through modulation of the gate terminal. This controllability facilitates the consistent manifestation of multistate memory effects. Notably, the memtransistor behavior becomes more significant as the channel length diminishes, particularly with channel lengths below 1.6 μm, showcasing an increase in the switching ratio alongside a decrease in the set voltage with the decreasing channel length. Our optimized memtransistor demonstrates the ability to exhibit individual resistance states spanning 5 orders of magnitude, with switching drain voltages of approximately 0.05 V. To elucidate these findings, we investigate hot carrier effects and their interplay with oxide traps within the HfO2 dielectric. This work highlights the importance of memtransisor behavior in highly scaled 2D transistors, particularly those featuring low contact resistances. This understanding holds the potential to tailor memory characteristics essential for the development of energy-efficient neuromorphic devices.

Selected Area Manipulation of MoS2 via Focused Electron Beam-Induced Etching for Nanoscale Device Editing

We demonstrate direct-write patterning of single and multilayer MoS2 via a focused electron beam-induced etching (FEBIE) process mediated with the XeF2 precursor. MoS2 etching is performed at various currents, areal doses, on different substrates, and characterized using scanning electron and atomic force microscopies as well as Raman and photoluminescence spectroscopies. Scanning transmission electron microscopy reveals a sub-40 nm etching resolution and the progression of point defects and lateral etching of the consequent unsaturated bonds. The results confirm that the electron beam-induced etching process is minimally invasive to the underlying material in comparison to ion beam techniques, which damage the subsurface material. Single-layer MoS2 field-effect transistors are fabricated, and device characteristics are compared for channels that are edited via the selected area etching process. The source-drain current at constant gate and source-drain voltage scale linearly with the edited channel width. Moreover, the mobility of the narrowest channel width decreases, suggesting that backscattered and secondary electrons collaterally affect the periphery of the removed area. Focused electron beam doses on single-layer transistors below the etching threshold were also explored as a means to modify/thin the channel layer. The FEBIE exposures showed demonstrative effects via the transistor transfer characteristics, photoluminescence spectroscopy, and Raman spectroscopy. While strategies to minimize backscattered and secondary electron interactions outside of the scanned regions require further investigation, here, we show that FEBIE is a viable approach for selective nanoscale editing of MoS2 devices.

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.

From Material to Cameras: Low-Dimensional Photodetector Arrays on CMOS

The last two decades have witnessed a dramatic increase in research on low-dimensional material with exceptional optoelectronic properties. While low-dimensional materials offer exciting new opportunities for imaging, their integration in practical applications has been slow. In fact, most existing reports are based on single-pixel devices that cannot rival the quantity and quality of information provided by massively parallelized mega-pixel imagers based on complementary metal-oxide semiconductor (CMOS) readout electronics. The first goal of this review is to present new opportunities in producing high-resolution cameras using these new materials. New photodetection methods and materials in the field are presented, and the challenges involved in their integration on CMOS chips for making high-resolution cameras are discussed. Practical approaches are then presented to address these challenges and methods to integrate low-dimensional material on CMOS. It is also shown that such integrations could be used for ultra-low noise and massively parallel testing of new material and devices. The second goal of this review is to present the colossal untapped potential of low-dimensional material in enabling the next-generation of low-cost and high-performance cameras. It is proposed that low-dimensional materials have the natural ability to create excellent bio-inspired artificial imaging systems with unique features such as in-pixel computing, multi-band imaging, and curved retinas.

Enhancement of silicon sub-bandgap photodetection by helium-ion implantation

Silicon sub-bandgap photodetectors can detect light at the infrared telecommunication wavelengths but with relatively weak photo-response. In this work, we demonstrate the enhancement of sub-bandgap photodetection in silicon by helium-ion implantation, without affecting the transparency that is an important beneficial feature of this type of photodetectors. With an implantation dose of 1 × 1013 ions/cm2, the minimal detectable optical power can be improved from - 33.2 to - 63.1 dBm, or, by 29.9 dB, at the wavelength of 1550 nm, and the photo-response at the same optical power (- 10 dBm) can be enhanced by approximately 18.8 dB. Our work provides a method for strategically modifying the intrinsic trade-off between transparency and strong photo-responses of this type of photodetectors.

Manipulation of the electrical and memory properties of MoS2 field-effect transistors by highly charged ion irradiation

Field-effect transistors based on molybdenum disulfide (MoS2) exhibit a hysteresis in their transfer characteristics, which can be utilized to realize 2D memory devices. This hysteresis has been attributed to charge trapping due to adsorbates, or defects either in the MoS2 lattice or in the underlying substrate. We fabricated MoS2 field-effect transistors on SiO2/Si substrates, irradiated these devices with Xe30+ ions at a kinetic energy of 180 keV to deliberately introduce defects and studied the resulting changes of their electrical and hysteretic properties. We find clear influences of the irradiation: while the charge carrier mobility decreases linearly with increasing ion fluence (up to only 20% of its initial value) the conductivity actually increases again after an initial drop of around two orders of magnitude. We also find a significantly reduced n-doping (≈1012 cm-2) and a well-developed hysteresis after the irradiation. The hysteresis height increases with increasing ion fluence and enables us to characterize the irradiated MoS2 field-effect transistor as a memory device with remarkably longer relaxation times (≈ minutes) compared to previous works.

Controllable Resistive Switching in ReS2 /WS2 Heterostructure for Nonvolatile Memory and Synaptic Simulation

Memristors with nonvolatile storage performance and simulated synaptic functions are regarded as one of the critical devices to overcome the bottleneck in traditional von Neumann computer architecture. 2D van der Waals heterostructures have paved a new way for the development of advanced memristors by integrating the intriguing features of different materials and offering additional controllability over their optoelectronic properties. Herein, planar memristors with both electrical and optical tunability based on ReS2 /WS2 van der Waals heterostructure are demonstrated. The devices show unique unipolar nonvolatile behavior with high Roff /Ron ratio of up to 106 , desirable endurance, and retention, which are superior to pure ReS2 and WS2 devices. When decreasing the channel length, the set voltage can be notably reduced while the high Roff /Ron ratios are retained. By introducing electrostatic doping through the gate control, the set voltage can be tailored in a wide range from 4.50 to 0.40 V. Furthermore, biological synaptic functions and plasticity, including spike rate-dependent plasticity and paired-pulse facilitation, are successfully realized. By employing optical illumination, resistive switching can also be modulated, which is dependent on the illumination energy and power. A mechanism related to the interlayer charge transfer controlled by optical excitation is revealed.

Unravelling the Data Retention Mechanisms under Thermal Stress on 2D Memristors

Memristors based on two-dimensional (2D) materials are a rapidly growing research area due to their potential in energy-efficient in-memory processing and neuromorphic computing. However, the data retention of these emerging memristors remains sparsely investigated, despite its crucial importance to device performance and reliability. In this study, we employ kinetic Monte-Carlo simulations to investigate the data retention of a 2D planar memristor. The operation of the memristor depends on field-driven on defect migration, while thermal diffusion gradually evens the defect distribution, leading to the degradation of the high resistance state (HRS) and diminishing the ON/OFF ratio. Notably, we examine the resilience of devices based on single crystals of transition metal dichalcogenides (TMDs) in harsh environments. Specifically, our simulations show that MoS2-based devices have negligible degradation after 10 years of thermal annealing at 400 K. Furthermore, the variability in data retention lifetime across different temperatures is less than 22%, indicating a relatively consistent performance over a range of thermal conditions. We also demonstrate that device miniaturization does not compromise data retention lifetime. Moreover, employing materials with higher activation energy for defect migration can significantly enhance data retention at the cost of increased switching voltage. These findings shed light on the behavior of 2D memristors and pave the way for their optimization in practical applications.

Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics

Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.

On the switching mechanism and optimisation of ion irradiation enabled 2D MoS2 memristors

Memristors are prominent passive circuit elements with promising futures for energy-efficient in-memory processing and revolutionary neuromorphic computation. State-of-the-art memristors based on two-dimensional (2D) materials exhibit enhanced tunability, scalability and electrical reliability. However, the fundamental of the switching is yet to be clarified before they can meet industrial standards in terms of endurance, variability, resistance ratio, and scalability. This new physical simulator based on the kinetic Monte Carlo (kMC) algorithm reproduces the defect migration process in 2D materials and sheds light on the operation of 2D memristors. The present work employs the simulator to study a two-dimensional 2H-MoS₂ planar resistive switching (RS) device with an asymmetric defect concentration introduced by ion irradiation. The simulations unveil the non-filamentary RS process and propose routes to optimize the device's performance. For instance, the resistance ratio can be increased by 53% by controlling the concentration and distribution of defects, while the variability can be reduced by 55% by increasing 5-fold the device size from 10 to 50 nm. Our simulator also explains the trade-offs between the resistance ratio and variability, resistance ratio and scalability, and variability and scalability. Overall, the simulator may enable an understanding and optimization of devices to expedite cutting-edge applications.

Pseudo-transistors for emerging neuromorphic electronics

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

Resistive Switching Crossbar Arrays Based on Layered Materials

Resistive switching (RS) devices are metal/insulator/metal cells that can change their electrical resistance when electrical stimuli are applied between the electrodes, and they can be used to store and compute data. Planar crossbar arrays of RS devices can offer a high integration density (>10⁸  devices mm^{- 2} ) and this can be further enhanced by stacking them three-dimensionally. The advantage of using layered materials (LMs) in RS devices compared to traditional phase-change materials and metal oxides is that their electrical properties can be adjusted with a higher precision. Here, the key figures-of-merit and procedures to implement LM-based RS devices are defined. LM-based RS devices fabricated using methods compatible with industry are identified and discussed. The focus is on small devices (size < 9 µm² ) arranged in crossbar structures, since larger devices may be affected by artifacts, such as grain boundaries and flake junctions. How to enhance device performance, so to accelerate the development of this technology, is also discussed.

Structure modulation of two-dimensional transition metal chalcogenides: recent advances in methodology, mechanism and applications

Together with the development of two-dimensional (2D) materials, transition metal dichalcogenides (TMDs) have become one of the most popular series of model materials for fundamental sciences and practical applications. Due to the ever-growing requirements of customization and multi-function, dozens of modulated structures have been introduced in TMDs. In this review, we present a systematic and comprehensive overview of the structure modulation of TMDs, including point, linear and out-of-plane structures, following and updating the conventional classification for silicon and related bulk semiconductors. In particular, we focus on the structural characteristics of modulated TMD structures and analyse the corresponding root causes. We also summarize the recent progress in modulating methods, mechanisms, properties and applications based on modulated TMD structures. Finally, we demonstrate challenges and prospects in the structure modulation of TMDs and forecast potential directions about what and how breakthroughs can be achieved.

Atomically Thin Synapse Networks on Van Der Waals Photo-Memtransistors

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems

Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2-x Heterostructure Memtransistor

Electrically tunable resistive switching of a polycrystalline MoS₂-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS₂ grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS₂/ZrO_2-x heterostructure. Compared to the control device with the MoS₂ semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO_2-x layer below the MoS₂, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS₂/ZrO_2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS₂/ZrO_2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.

Physical Insights into Vacancy-Based Memtransistors: Toward Power Efficiency, Reliable Operation, and Scalability

Memtransistors that combine the properties of transistor and memristor hold significant promise for in-memory computing. While superior data storage capability is achieved in memtransistors through gate voltage-induced conductance modulation, the lateral device configuration would not only result in high write bias, which compromises the power efficiency, but also suffers from unsuccessful memory reset that leads to reliability concerns. To circumvent such performance limitations, an advanced physics-based model is required to uncover the dynamic resistive switching behavior and deduce the key driving parameters for the switching process. This work demonstrates a self-consistent physics-based model which incorporates the often-overlooked effects of lattice temperature, vacancy dynamics, and channel electrostatics to accurately solve the interaction between gate potential, ions, and carriers on the memristive switching mechanism. The completed model is carefully calibrated with an ambipolar WSe2 memtransistor and hence enables the investigation of the carrier polarity effect (electrons vs holes) on vacancy transport. Nevertheless, the validity of the model can be extended to different materials by a simple material-dependent parameter modification. Building upon the existing understanding of Schottky barrier height modulation, our study reveals three key insights─leveraging threshold voltage shifts to lower write bias; optimizing lattice temperature distribution and read bias polarity to achieve successful memory state recovery; engineering contact work function to overcome the detrimental parasitic current flow in short channel ambipolar memtransistors. Therefore, understanding the significant correlation between the switching mechanisms, different material systems, and device structures allows performance optimization of operating modes and device designs for future memtransistors-based computing systems.

Versatile Approach of Silicon Nanofabrication without Resists: Helium Ion-Bombardment Enhanced Etching

Herein, we report a helium ion-bombardment enhanced etching method for silicon nanofabrication without the use of resists; furthermore, we demonstrate its unique advantages for straightforward fabrication on irregular surfaces and prototyping nano-electro-mechanical system devices, such as self-enclosed Si nanofluidic channels and mechanical nano-resonators. This method employs focused helium ions to selectively irradiate single-crystal Si to disrupt the crystal lattice and transform it into an amorphous phase that can be etched at a rate 200 times higher than that of the non-irradiated Si. Due to the unique raindrop shape of the interaction volumes between helium ions and Si, buried Si nanofluidic channels can be constructed using only one dosing step, followed by one step of conventional chemical etching. Moreover, suspended Si nanobeams can be fabricated without an additional undercut step for release owing to the unique raindrop shape. In addition, we demonstrate nanofabrication directly on 3D micro/nano surfaces, such as an atomic force microscopic probe, which is challenging for conventional nanofabrication due to the requirement of photoresist spin coating. Finally, this approach can also be extended to assist in the etching of other materials that are difficult to etch, such as silicon carbide (SiC).

Low-Power Memristor Based on Two-Dimensional Materials

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.

Wafer-Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy-Efficient Neural Network Hardware

Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe₂ ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.

Recent Advances on Tuning the Interlayer Coupling and Properties in van der Waals Heterostructures

2D van der Waals (vdW) heterostructures are receiving increasing research attention due to the theoretically amazing properties and unprecedented application potential. However, the as-synthesized heterostructures are generally underperforming due to the weak interlayer coupling, which inspires the researchers to find ways to modulate the interlayer coupling and properties, realizing the tailored performance for actual applications. There have been a lot of publications regarding the controllable regulation of the structures and properties of 2D vdW heterostructures in the past few years, while a review work summarizing the current advances is not yet available, though it is significant. This paper conducts a state-of-the-art review regarding the current research progress of performance modulation of vdW heterostructures by different techniques. First, the general synthesis methods of vdW heterostructures are summarized. Then, different performance modulation techniques, that is, mechanical-based, external fields-assisted, and particle beam irradiation-based methods, are discussed and compared in detail. Some of the newly proposed concepts are described. Thereafter, applications of vdW heterostructures with tailored properties are reviewed for the application prospects of the topic around this area. Moreover, the future research challenges and prospects are discussed, aiming at triggering more research interest and device applications around this topic.

High-Performance Memristors Based on Ultrathin 2D Copper Chalcogenides

Copper chalcogenides represent a class of materials with unique crystal structures, high electrical conductivity, and earth abundance, and are recognized as promising candidates for next-generation green electronics. However, their 2D structures and the corresponding electronic properties have rarely been touched. Herein, a series of ultrathin copper chalcogenide nanosheets with thicknesses down to two unit cells are successfully synthesized, including layered Cu₂ Te, as well as nonlayered CuSe and Cu₉ S₅ , via van der Waals epitaxy, and their nonvolatile memristive behavior is investigated for the first time. Benefiting from the highly active Cu ions with low migration barriers, the memristors based on ultrathin 2D copper chalcogenide crystals exhibit relatively small switching voltage (≈0.4 V), fast switching speed, high switching uniformity, and wide operating temperature range (from 80 to 420 K), as well as stable retention and good cyclic endurance. These results demonstrate their tangible applications in future low-power, cryogenic, and high temperature harsh electronics.

In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition accuracy

The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate indium gallium zinc oxide (IGZO) synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.

Graphene/Ferroelectric (Ge-Doped HfO2) Adaptable Transistors Acting as Reconfigurable Logic Gates

We present an array of 225 field-effect transistors (FETs), where each of them has a graphene monolayer channel grown on a 3-layer deposited stack of 22 nm control HfO₂/5 nm Ge-HfO₂ intermediate layer/8 nm tunnel HfO₂/p-Si substrate. The intermediate layer is ferroelectric and acts as a floating gate. All transistors have two top gates, while the p-Si substrate is acting as a back gate. We show that these FETs are acting memtransistors, working as two-input reconfigurable logic gates with memory, the type of the logic gate depending only on the values of the applied gate voltages and the choice of a threshold current.

Electrically and Optically Controllable p-n Junction Memtransistor Based on an Al2 O3 Encapsulated 2D Te/ReS2 van der Waals Heterostructure

The exploration of memtransistors as a combination of a memristor and a transistor has recently attracted intensive attention because it offers a promising candidate for next-generation multilevel nonvolatile memories and synaptic devices. However, the present state-of-the-art memtransistors, which are based on a single material, such as MoS₂ or perovskite, exhibit a relatively low switching ratio, require extremely high electric fields to modulate bistable resistance states and do not perform multifunctional operations. Here, the realization of an electrically and optically controllable p-n junction memtransistor using an Al₂ O₃ encapsulated 2D Te/ReS₂ van der Waals heterostructure is reported. The hybrid memtransistor shows a reversible bipolar resistance switching behavior between a low resistance state and a high resistance state with a high switching ratio up to 10⁶ at a low operating voltage (<10 V), high cycling endurance, and long retention time. Moreover, multiple resistance states are achieved by applying different bias voltages, gate voltages, or light powers. In addition, logical operations, including the inverter and AND/OR gates, and synaptic activities are performed by controlling the optical and electrical inputs. The work offers a novel strategy for the reliable fabrication of p-n junction memtransistors for multifunctional devices and neuromorphic applications.

Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in computational neuroscience have demonstrated the importance of heterosynaptic plasticity for network activity regulation and memorization. Implementing heterosynaptic plasticity in hardware is thus highly desirable, but important materials and engineering challenges remain, calling for breakthroughs in neuromorphic devices. In this mini-review, we propose an overview of the latest advances in multi-terminal memristive devices on silicon with tunable synaptic plasticity, enabling heterosynaptic plasticity in hardware. The scalability and compatibility of the devices with industrial complementary metal oxide semiconductor (CMOS) technologies are discussed.

Artificial Synapse Based on a 2D-SnO2 Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing

A new type of two-dimensional (2D) SnO2 semiconductor-based gate-tunable memristor, that is, a memtransistor, an integrated device of a memristor and a transistor, was demonstrated to advance next-generation neuromorphic computing technology. The polycrystalline 2D-SnO2 memristors derived from a low-temperature and vacuum-free liquid metal process offer several interesting resistive switching properties such as excellent digital/analog resistive switching, multistate storage, and gate-tunability function of resistance switching states. Significantly, the gate tunability function that is not achievable in conventional two-terminal memristors provides the capability to implement heterosynaptic analog switching by regulating gate bias for enabling complex neuromorphic learning. We successfully demonstrated that the gate-tunable synaptic device dynamically modulated the analog switching behavior with good linearity and an improved conductance change ratio for high recognition accuracy learning. The presented gate-tunable 2D-oxide memtransistor will advance neuromorphic device technology and open up new opportunities to design learning schemes with an extra degree of freedom.

Large-Area MoS2 via Colloidal Nanosheet Ink for Integrated Memtransistor

2D transition metal dichalcogenides (TMDs) exhibit intriguing properties for applications in optoelectronics and electronics, among which memtransistors received extensive attention as multifunctional devices. For practical applications of 2D TMDs, large-area fabrication of the materials via reliable processes, which is in trade-off with their quality, has been a long-standing issue. Here, a simple and effective way is proposed to fabricate large-area and high-quality molybdenum disulfide thin films using MoS₂ colloidal ink through a spray coating, followed by a postsulfurization process. High-quality MoS₂ thin films exhibit excellent optical and electrical properties that can be utilized in field-effect transistors (FETs) and memtransistor arrays. The MoS₂ FETs show an average on/off ratio of 5 × 10⁶ and a high electron mobility of 10.34 cm²  V^-1  s^-1 , which can be understood by the healing of sulfur vacancies, recrystallization, and the removal of the carbon contamination of the MoS₂ . These MoS₂ -based memtransistors present stable operations with a high switching ratio tuned by back gate and light illumination, which is promising for multiple-levels memory and complex neuromorphic computing. This study demonstrates a new strategy to fabricate 2D TMDs with large-area and high quality for integrated optoelectronic and memory device applications.

Reconfigurable 2D WSe2 -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity

The mimicking of both homosynaptic and heterosynaptic plasticity using a high-performance synaptic device is important for developing human-brain-like neuromorphic computing systems to overcome the ever-increasing challenges caused by the conventional von Neumann architecture. However, the commonly used synaptic devices (e.g., memristors and transistors) require an extra modulate terminal to mimic heterosynaptic plasticity, and their capability of synaptic plasticity simulation is limited by the low weight adjustability. In this study, a WSe₂ -based memtransistor for mimicking both homosynaptic and heterosynaptic plasticity is fabricated. By applying spikes on either the drain or gate terminal, the memtransistor can mimic common homosynaptic plasticity, including spiking rate dependent plasticity, paired pulse facilitation/depression, synaptic potentiation/depression, and filtering. Benefitting from the multi-terminal input and high adjustability, the resistance state number and linearity of the memtransistor can be improved by optimizing the conditions of the two inputs. Moreover, the device can successfully mimic heterosynaptic plasticity without introducing an extra terminal and can simultaneously offer versatile reconfigurability of excitatory and inhibitory plasticity. These highly adjustable and reconfigurable characteristics offer memtransistors more freedom of choice for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

Grain-Boundary Engineering of Monolayer MoS2 for Energy-Efficient Lateral Synaptic Devices

Synaptic devices based on 2D-layered materials have emerged as high-efficiency electronic synapses and neurons for neuromorphic computing. Lateral 2D synaptic devices have the advantages of multiple functionalities by responding to diverse stimuli, but they consume large amounts of energy, far more than the human brain. Moreover, current lateral devices employ several mechanisms based on conductive filaments and grain boundaries (GBs), but their formation is random and difficult to control, also hindering their practical applications. Here, four-terminal, lateral synaptic devices with artificially engineered GBs are reported, which are made from monolayer MoS₂ . With lithography-free, direct-laser-writing-controlled MoS₂ /MoS_{2- x} O_δ GBs, such synaptic devices exhibit short-term and long-term plasticity characteristics that are responsive to electric and light stimulation simultaneously. This enables detailed simulations of biological learning and cognitive processes as well as image perception and processing. In particular, the device exhibits low energy consumption, similar to that of the human brain and much lower than those of other lateral 2D synaptic devices. This work provides an effective way to fabricate lateral synaptic devices for practical application development and sheds light on controllable electrical state switching for neuromorphic computing.

Direct visualization of beam-resist interaction volume for sub-nanometer helium ion beam-lithography

Interaction volume of beam-resist is a basis unit of beam-lithography, directly determines the critical parameters of beam-lithography. We have visualized the interaction volume at the state-of-the-art sub-10 nm scale by a spot irradiation of sub-nanometer helium ion beam into an approximately free-standing resist. The visualized interaction volume suggests helium ion beam has an excellent capability in nanofabrication. Specifically, helium ion beam-lithography is 1000 times more efficient than electron beam-lithography (EBL), owns a sub-4 nm resolution, can achieve a large pattern aspect ratio (greater than 8), and does not suffer from backscattering effect at a normal exposure dose. Furthermore, the interaction volume has been theoretically studied by considering the spatial distribution of energy deposited in the resist, and eventually lead to a model for pattern prediction and proximity effect corrections. We expect that, our approach to visualize the interaction volume may be applied to study other high resolution lithographic techniques such as x-ray lithography and EBL, and it may open new possibilities in other applications, like beam-imaging, beam-milling, and beam-modification.

A review of defect engineering, ion implantation, and nanofabrication using the helium ion microscope

The helium ion microscope has emerged as a multifaceted instrument enabling a broad range of applications beyond imaging in which the finely focused helium ion beam is used for a variety of defect engineering, ion implantation, and nanofabrication tasks. Operation of the ion source with neon has extended the reach of this technology even further. This paper reviews the materials modification research that has been enabled by the helium ion microscope since its commercialization in 2007, ranging from fundamental studies of beam-sample effects, to the prototyping of new devices with features in the sub-10 nm domain.

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.

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.

Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed.

Self-Selective Multi-Terminal Memtransistor Crossbar Array for In-Memory Computing

Two-terminal resistive switching devices are commonly plagued with longstanding scientific issues including interdevice variability and sneak current that lead to computational errors and high-power consumption. This necessitates the integration of a separate selector in a one-transistor-one-RRAM (1T-1R) configuration to mitigate crosstalk issue, which compromises circuit footprint. Here, we demonstrate a multi-terminal memtransistor crossbar array with increased parallelism in programming via independent gate control, which allows in situ computation at a dense cell size of 3-4.5 F² and a minimal sneak current of 0.1 nA. Moreover, a low switching energy of 20 fJ/bit is achieved at a voltage of merely 0.42 V. The architecture is capable of performing multiply-and-accumulate operation, a core computing task for pattern classification. A high MNIST recognition accuracy of 96.87% is simulated owing to the linear synaptic plasticity. Such computing paradigm is deemed revolutionary toward enabling data-centric applications in artificial intelligence and Internet-of-things.

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.

Wafer-scale graphene-ferroelectric HfO2/Ge-HfO2/HfO2 transistors acting as three-terminal memristors

In this paper we report a set of experiments at the wafer level regarding field-effect transistors with a graphene monolayer channel transferred on the ferroelectric HfO₂/Ge-HfO₂/HfO₂ three-layer structure. This kind of transistor has a switching ratio of 10³ between on and off states due to the bandgap in graphene induced by the ferroelectric structure. Both top and back gates effectively control the carriers' charge flow in graphene. The transistor acts as a three-terminal memristor, termed a memtransistor, with applications in neuromorphic computation.

Imaging and milling resolution of light ion beams from helium ion microscopy and FIBs driven by liquid metal alloy ion sources

While the application of focused ion beam (FIB) techniques has become a well-established technique in research and development for patterning and prototyping on the nanometer scale, there is still a large underused potential with respect to the usage of ion species other than gallium. Light ions in the range of m = 1-28 u (hydrogen to silicon) are of increasing interest due to the available high beam resolution in the nanometer range and their special chemical and physical behavior in the substrate. In this work, helium and neon ion beams from a helium ion microscope are compared with ion beams such as lithium, beryllium, boron, and silicon, obtained from a mass-separated FIB using a liquid metal alloy ion source (LMAIS) with respect to the imaging and milling resolution, as well as the current stability. Simulations were carried out to investigate whether the experimentally smallest ion-milled trenches are limited by the size of the collision cascade. While He⁺ offers, experimentally and in simulations, the smallest minimum trench width, light ion species such as Li⁺ or Be⁺ from a LMAIS offer higher milling rates and ion currents while outperforming the milling resolution of Ne⁺ from a gas field ion source. The comparison allows one to select the best possible ion species for the specific demands in terms of resolution, beam current, and volume to be drilled.

Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications

Two-dimensional (2D) layered materials and their heterostructures have recently been recognized as promising building blocks for futuristic brain-like neuromorphic computing devices. They exhibit unique properties such as near-atomic thickness, dangling-bond-free surfaces, high mechanical robustness, and electrical/optical tunability. Such attributes unattainable with traditional electronic materials are particularly promising for high-performance artificial neurons and synapses, enabling energy-efficient operation, high integration density, and excellent scalability. In this review, diverse 2D materials explored for neuromorphic applications, including graphene, transition metal dichalcogenides, hexagonal boron nitride, and black phosphorous, are comprehensively overviewed. Their promise for neuromorphic applications are fully discussed in terms of material property suitability and device operation principles. Furthermore, up-to-date demonstrations of neuromorphic devices based on 2D materials or their heterostructures are presented. Lastly, the challenges associated with the successful implementation of 2D materials into large-scale devices and their material quality control will be outlined along with the future prospect of these emergent materials.

Effect of localized helium ion irradiation on the performance of synthetic monolayer MoS2 field-effect transistors

Helium ion irradiation is a known method of tuning the electrical conductivity and charge carrier mobility of novel two-dimensional semiconductors. Here, we report a systematic study of the electrical performance of chemically synthesized monolayer molybdenum disulfide (MoS₂) field-effect transistors irradiated with a focused helium ion beam as a function of increasing areal irradiation coverage. We determine an optimal coverage range of approx. 10%, which allows for the improvement of both the carrier mobility in the transistor channel and the electrical conductance of the MoS₂, due to doping with ion beam-created sulfur vacancies. Larger areal irradiations introduce a higher concentration of scattering centers, hampering the electrical performance of the device. In addition, we find that irradiating the electrode-channel interface has a deleterious impact on charge transport when contrasted with irradiations confined only to the transistor channel.

Memtransistors Based on Nanopatterned Graphene Ferroelectric Field-Effect Transistors

The ultimate memristor, which acts as resistive memory and an artificial neural synapse, is made from a single atomic layer. In this manuscript, we present experimental evidence of the memristive properties of a nanopatterned ferroelectric graphene field-effect transistor (FET). The graphene FET has, as a channel, a graphene monolayer transferred onto an HfO₂-based ferroelectric material, the channel being nanopatterned with an array of holes with a diameter of 20 nm.

Neuromorphic nanoelectronic materials

Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.

Two-dimensional materials for next-generation computing technologies

Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal-oxide-semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies.