A van der Waals Integrated Damage-Free Memristor Based on Layered 2D Hexagonal Boron Nitride

A hBN/Ga2O3 pn junction diode

The development of next-generation materials such as hBN and Ga2O3 remains a topic of intense focus owing to their suitability for efficient deep ultraviolet (DUV) emission and power electronic applications. In this study, we combine p-type hBN and n-type Ga2O3, forming a pseudo-vertical pn hBN/Ga2O3 heterojunction device. Rectification ratios > 105 (300 K) and [Formula: see text]400 (475 K) are observed and are amongst the highest values reported to date for ultra-thin hBN-based pn junctions. The measured current under forward bias is ~2 mA, which we attribute to the shallow Mg acceptor level (60 meV), and 0.2 µA at -10 V. Critically, device performance remains stable and highly repeatable after a multitude of temperature ramps to 475 K. Capacitance-voltage measurements indicate widening the depletion region under increasing reverse bias voltage and a built-in voltage of 2.34 V is recorded. The hBN p-type characteristic is confirmed by Hall effect, a hole concentration of [Formula: see text] cm-3 and mobility of 24.8 cm2/Vs is achieved. Mg doped hBN resistance reduces by >108 compared to intrinsic material. Future work shall focus on the optical emission properties of this material system.

Thermally Oxidized Memristor and 1T1R Integration for Selector Function and Low-Power Memory

Resistive switching memories have garnered significant attention due to their high-density integration and rapid in-memory computing beyond von Neumann's architecture. However, significant challenges are posed in practical applications with respect to their manufacturing process complexity, a leakage current of high resistance state (HRS), and the sneak-path current problem that limits their scalability. Here, a mild-temperature thermal oxidation technique for the fabrication of low-power and ultra-steep memristor based on Ag/TiOx/SnOx/SnSe2/Au architecture is developed. Benefiting from a self-assembled oxidation layer and the formation/rupture of oxygen vacancy conductive filaments, the device exhibits an exceptional threshold switching behavior with high switch ratio exceeding 106, low threshold voltage of ≈1 V, long-term retention of >104 s, an ultra-small subthreshold swing of 2.5 mV decade-1 and high air-stability surpassing 4 months. By decreasing temperature, the device undergoes a transition from unipolar volatile to bipolar nonvolatile characteristics, elucidating the role of oxygen vacancies migration on the resistive switching process. Further, the 1T1R structure is established between a memristor and a 2H-MoTe2 transistor by the van der Waals (vdW) stacking approach, achieving the functionality of selector and multi-value memory with lower power consumption. This work provides a mild-thermal oxidation technology for the low-cost production of high-performance memristors toward future in-memory computing applications.

All-2D-Materials Subthreshold-Free Field-Effect Transistor with Near-Ideal Switching Slope

The Boltzmann Tyranny, set by thermionic statistics, dictates the lower limit of switching slope (SS) of a MOSFET to be 60 mV/dec, the fundamental barrier for low-dissipative electronics. The large SS leads to nonscalable voltage, significant leakage, and power consumption, particularly at short channels, making transistor scaling an intimidating challenge. In recent decades, an array of steep-slope transistors has been proposed; none is close to an ideal switch with ultimately abrupt switching (SS ∼ 0 mV/dec) between the binary logic states. We demonstrated an all-2D-materials van-der-Waals-heterostructure (vdW)-based FET that exhibits ultrasteep switching (0.33 mV/dec), a large on/off current ratio (∼107), and an ultralow off current (∼0.1 pA). The "Subthreshold-Free" operation achieved by the collective behavior of functional materials enables FET switching directly from the OFF-state to the ON-state with entirely eliminated subthreshold region, behaving as the ideal logic switch. Two-inch wafer-scale device fabrication is demonstrated. Boosted by device innovation and emerging materials, the research presents an advancement in achieving the "beyond-Boltzmann" transistors, overcoming one of the CMOS electronics' most infamous technology barriers that have plagued the research community for decades.

Two-dimensional material-based memristive devices for alternative computing

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.

Quantum Conductance in Vertical Hexagonal Boron Nitride Memristors with Graphene-Edge Contacts

Two-dimensional materials (2DMs) have gained significant interest for resistive-switching memory toward neuromorphic and in-memory computing (IMC). To achieve atomic-level miniaturization, we introduce vertical hexagonal boron nitride (h-BN) memristors with graphene edge contacts. In addition to enabling three-dimensional (3D) integration (i.e., vertical stacking) for ultimate scalability, the proposed structure delivers ultralow power by isolating single conductive nanofilaments (CNFs) in ultrasmall active areas with negligible leakage thanks to atomically thin (∼0.3 nm) graphene edge contacts. Moreover, it facilitates studying fundamental resistive-switching behavior of single CNFs in CVD-grown 2DMs that was previously unattainable with planar devices. This way, we studied their programming characteristics and observed a consistent single quantum step in conductance attributed to unique atomically constrained nanofilament behavior in CVD-grown 2DMs. This resistive-switching property was previously suggested for h-BN memristors and linked to potential improvements in stability (robustness of CNFs), and now we show experimental evidence including superior retention of quantized conductance.

Selective CW Laser Synthesis of MoS2 and Mixture of MoS2 and MoO2 from (NH4)2MoS4 Film

Very recently, the synthesis of 2D MoS2 and WS2 through pulsed laser-directed thermolysis can achieve wafer-scale and large-area structures, in ambient conditions. In this paper, we report the synthesis of MoS2 and MoS2 oxides from (NH4)2MoS4 film using a visible continuous-wave (CW) laser at 532 nm, instead of the infrared pulsed laser for the laser-directed thermolysis. The (NH4)2MoS4 film is prepared by dissolving its crystal powder in DI water, sonicating the solution, and dip-coating onto a glass slide. We observed a laser intensity threshold for the laser synthesis of MoS2, however, it occurred in a narrow laser intensity range. Above that range, a mixture of MoS2 and MoO2 is formed, which can be used for a memristor device, as demonstrated by other research groups. We did not observe a mixture of MoS2 and MoO3 in the laser thermolysis of (NH4)2MoS4. The laser synthesis of MoS2 in a line pattern is also achieved through laser scanning. Due to of the ease of CW beam steering and the fine control of laser intensities, this study can lead toward the CW laser-directed thermolysis of (NH4)2MoS4 film for the fast, non-vacuum, patternable, and wafer-scale synthesis of 2D MoS2.

Ultrahigh Photosensitivity Based on Single-Step Lay-on Integration of Freestanding Two-Dimensional Transition-Metal Dichalcogenide

Two-dimensional transition-metal dichalcogenides have attracted significant attention because of their unique intrinsic properties, such as high transparency, good flexibility, atomically thin structure, and predictable electron transport. However, the current state of device performance in monolayer transition-metal dichalcogenide-based optoelectronics is far from commercialization, because of its substantial strain on the heterogeneous planar substrate and its robust metal deposition, which causes crystalline damage. In this study, we show that strain-relaxed and undamaged monolayer WSe2 can improve a device performance significantly. We propose here an original point-cell-type photodetector. The device consists in a monolayer of an absorbing TMD (i.e., WSe2) simply deposited on a structured electrode, i.e., core-shell silicon-gold nanopillars. The maximum photoresponsivity of the device is found to be 23.16 A/W, which is a significantly high value for monolayer WSe2-based photodetectors. Such point-cell photodetectors can resolve the critical issues of 2D materials, leading to tremendous improvements in device performance.

Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin

The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.

Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ ∼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

In Situ Synthesis of Two-Dimensional Lateral Semiconducting-Mo:Se//Metallic-Mo Junctions Using Controlled Diffusion of Se for High-Performance Large-Scaled Memristor

Two-dimensional (2D) materials are favorable candidates for resistive memories in high-density nanoelectronics owing to their ultrathin scaling and controllable interfacial characteristics. However, high processing temperatures and difficulties in mechanical transfer are intriguing challenges associated with their implementation in large areas with crossbar architecture. A high processing temperature may damage the electrical functionalities of the bottom electrode, and mechanical transfer of 2D materials may introduce undesirable microscopic defects and macroscopic discontinuities. In this study, an in situ fabrication of an electrode and 2D-molybdenum diselenide (MoSe₂) is reported. The controlled diffusion of selenium (Se) in the predeposited molybdenum (Mo) produces Mo//Mo:Se stacks with a few layers of MoSe₂ on top and MoSe_x on the bottom. Diffusion-assisted Mo//Mo:Se fabrication is observed over a large area (4 in. wafer). Additionally, a 5 × 5 array of crossbar memristors (Mo//Mo:Se//Ag) is fabricated using the diffusion of Se in patterned Mo. These memristors exhibit a small switching voltage (∼1.1 V), high endurance (>250 cycles), and excellent retention (>15 000 s) with minimum cycle-to-cycle and device-to-device variation. Thus, the proposed nondestructive in situ technique not only simplifies the fabrication but also minimizes the number of required stages.

Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors

Two-dimensional (2D) materials have become potential resistive switching (RS) layers to prepare emerging non-volatile memristors. The atomically thin thickness and the highly controllable defect density contribute to the construction of ultimately scaled memory cells with stable switching behaviors. Although the conductive bridge random-access memory based on 2D hexagonal boron nitride has been widely studied, the realization of RS completely relying on vacancies in 2D materials has performance superiority. Here, we synthesize carbon-doped h-BN (C-h-BN) with a certain number of defects by controlling the weight percentage of carbon powder in the source. These defects can form a vacancy-based conductive filament under an applied electric field. The memristor displays bipolar non-volatile memory with a low SET voltage of 0.85 V and shows a long retention time of up to 10⁴ s at 120 °C. The response times of the SET and RESET process are less than 80 ns and 240 ns, respectively. The current mapping by conductive atomic force microscopy demonstrates the electric-field-induced current tunneling from defective sites of the C-h-BN flake, revealing the defect-based RS in the C-h-BN memristor. Moreover, C-h-BN with excellent flexibility can be applied to wearable devices, maintaining stable RS performance in a variety of bending environments and after multiple bending cycles. The vacancy-based 2D memristor provides a new strategy for developing ultra-scaled memory units with high controllability.

A review of memristor: material and structure design, device performance, applications and prospects

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks

Hardware-based neural networks (NNs) can provide a significant breakthrough in artificial intelligence applications due to their ability to extract features from unstructured data and learn from them. However, realizing complex NN models remains challenging because different tasks, such as feature extraction and classification, should be performed at different memory elements and arrays. This further increases the required number of memory arrays and chip size. Here, we propose a three-dimensional ferroelectric NAND (3D FeNAND) array for the area-efficient hardware implementation of NNs. Vector-matrix multiplication is successfully demonstrated using the integrated 3D FeNAND arrays, and excellent pattern classification is achieved. By allocating each array of vertical layers in 3D FeNAND as the hidden layer of NN, each layer can be used to perform different tasks, and the classification of color-mixed patterns is achieved. This work provides a practical strategy to realize high-performance and highly efficient NN systems by stacking computation components vertically.

Hexagonal Boron Nitride for Next-Generation Photonics and Electronics

Hexagonal boron nitride (h-BN), an insulating 2D layered material, has recently attracted tremendous interest motivated by the extraordinary properties it shows across the fields of optoelectronics, quantum optics, and electronics, being exotic material platforms for various applications. At an early stage of h-BN research, it is explored as an ideal substrate and insulating layers for other 2D materials due to its atomically flat surface that is free of dangling bonds and charged impurities, and its high thermal conductivity. Recent discoveries of structural and optical properties of h-BN have expanded potential applications into emerging electronics and photonics fields. h-BN shows a very efficient deep-ultraviolet band-edge emission despite its indirect-bandgap nature, as well as stable room-temperature single-photon emission over a wide wavelength range, showing a great potential for next-generation photonics. In addition, h-BN is extensively being adopted as active media for low-energy electronics, including nonvolatile resistive switching memory, radio-frequency devices, and low-dielectric-constant materials for next-generation electronics.

Recent Progress in Contact Engineering of Field-Effect Transistor Based on Two-Dimensional Materials

Two-dimensional (2D) semiconductors have been considered as promising candidates to fabricate ultimately scaled field-effect transistors (FETs), due to the atomically thin thickness and high carrier mobility. However, the performance of FETs based on 2D semiconductors has been limited by extrinsic factors, including high contact resistance, strong interfacial scattering, and unintentional doping. Among these challenges, contact resistance is a dominant issue, and important progress has been made in recent years. In this review, the Schottky-Mott model is introduced to show the ideal Schottky barrier, and we further discuss the contribution of the Fermi-level pinning effect to the high contact resistance in 2D semiconductor devices. In 2D FETs, Fermi-level pinning is attributed to the high-energy metal deposition process, which would damage the lattice of atomically thin 2D semiconductors and induce the pinning of the metal Fermi level. Then, two contact structures and the strategies to fabricate low-contact-resistance short-channel 2D FETs are introduced. Finally, our review provides practical guidelines for the realization of high-performance 2D-semiconductors-based FETs with low contact resistance and discusses the outlook of this field.

2D Layers of Group VA Semiconductors: Fundamental Properties and Potential Applications

Members of the 2D group VA semiconductors (phosphorene, arsenene, antimonene, and bismuthine) present a new class of 2D materials, which are recently gaining a lot of research interest. These materials possess layered morphology, tunable direct bandgap, high charge carrier mobility, high stability, unique in-plane anisotropy, and negative Poisson's ratio. They prepare the ground for novel and multifunctional applications in electronics, optoelectronics, and batteries. The most recent analytical and empirical developments in the fundamental characteristics, fabrication techniques, and potential implementation of 2D group VA materials in this review, along with presenting insights and concerns for the field's future are analyzed.

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.