Stacked Two-Dimensional MXene Composites for an Energy-Efficient Memory and Digital Comparator

Recent developments of artificial intelligence in MXene-based devices: from synthesis to applications

Two-dimensional transition metal carbides, nitrides, or carbonitrides (MXenes) have garnered remarkable attention in various energy and environmental applications due to their high electrical conductivity, good thermal properties, large surface area, high mechanical strength, rapid charge transport mechanism, and tunable surface properties. Recently, artificial intelligence has been considered an emerging technology, and has been widely used in materials science, engineering, and biomedical applications due to its high efficiency and precision. In this review, we focus on the role of artificial intelligence-based technology in MXene-based devices and discuss the latest research directions of artificial intelligence in MXene-based devices, especially the use of artificial intelligence-based modeling tools for energy storage devices, sensors, and memristors. In addition, emphasis is given to recent progress made in synthesis methods for various MXenes and their advantages and disadvantages. Finally, the review ends with several recommendations and suggestions regarding the role of artificial intelligence in fabricating MXene-based devices. We anticipate that this review will provide guidelines on future research directions suitable for practical applications.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

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.

A carbon conductive filament-induced robust resistance switching behavior for brain-inspired computing

Memristors have revolutionized the path forward for brain-inspired computing. However, the instability of the nucleation process of conductive filaments based on active metal electrodes leads to the discrete distribution of switching parameters, which hinders the realization of high-performance and low-power devices for neuromorphic computing. In response, a carbon conductive filament-induced robust memristor is demonstrated with variation coefficients as low as 3.9%/-1.18%, a threshold power as low as 10-9 W, and 3 × 106 s retention and 107 cycle endurance behaviors can be maintained. The recognition accuracy for Modified National Institute of Standards and Technology (MNIST) handwriting is as high as 96.87%, attributed to the high linearity of the iterative updating of synaptic weights. The demodulation and storage functions of the American Standard Code for Information Interchange (ASCII) are demonstrated by programmable pulse modulation. Notably, the transmission electron microscopy (TEM) images allow the observation of carbon conductive filament paths formed in the low resistance state. First-principles calculations analyze the energetics of defects involved in the diffusion of carbon atoms into MoTe2 films. This work presents a novel guideline for studying memristor-based neuromorphic computing.

Digital image processing realized by memristor-based technologies

Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal-oxide-semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing.

Tunable Resistive Switching in 2D MXene Ti3C2 Nanosheets for Non-Volatile Memory and Neuromorphic Computing

An artificial synapse is essential for neuromorphic computing which has been expected to overcome the bottleneck of the traditional von-Neumann system. Memristors can work as an artificial synapse owing to their tunable non-volatile resistance states which offer the capabilities of information storage, processing, and computing. In this work, memristors based on two-dimensional (2D) MXene Ti₃C₂ nanosheets sandwiched by Pt electrodes are investigated in terms of resistive switching (RS) characteristics, synaptic functions, and neuromorphic computing. Digital and analog RS behaviors are found to coexist depending on the magnitude of operation voltage. Digital RS behaviors with two resistance states possessing a large switching ratio exceeding 10³ can be achieved under a high operation voltage. Analog RS behaviors with a series of resistance states exhibiting a gradual change can be observed at a relatively low operation voltage. Furthermore, artificial synapses can be implemented based on the memristors with the basic synaptic functions, such as long-term plasticity of long-term potentiation and depression and short-term plasticity of the paired-pulse facilitation and depression. Moreover, the "learning-forgetting" experience is successfully emulated based on the artificial synapses. Also, more importantly, the artificial synapses can construct an artificial neural network to implement image recognition. The coexistence of digital and analog RS behaviors in the 2D Ti₃C₂ nanosheets suggests the potential applications in non-volatile memory and neuromorphic computing, which is expected to facilitate simplifying the manufacturing complexity for complex neutral systems where analog and digital switching is essential for information storage and processing.

Polarization-sensitive optical responses from natural layered hydrated sodium sulfosalt gerstleyite

Multi-element layered materials have gained substantial attention in the context of achieving the customized light-matter interactions at subwavelength scale via stoichiometric engineering, which is crucial for the realization of miniaturized polarization-sensitive optoelectronic and nanophotonic devices. Herein, naturally occurring hydrated sodium sulfosalt gerstleyite is introduced as one new multi-element van der Waals (vdW) layered material. The mechanically exfoliated thin gerstleyite flakes are demonstrated to exhibit polarization-sensitive anisotropic linear and nonlinear optical responses including angle-resolved Raman scattering, anomalous wavelength-dependent linear dichroism transition, birefringence effect, and polarization-dependent third-harmonic generation (THG). Furthermore, the third-order nonlinear susceptibility of gerstleyite crystal is estimated by the probed flake thickness-dependent THG response. We envisage that our findings in the context of polarization-sensitive light-matter interactions in the exfoliated hydrated sulfosalt layers will be a valuable addition to the vdW layered material family and will have many implications in compact waveplates, on-chip photodetectors, optical sensors and switches, integrated photonic circuits, and nonlinear signal processing applications.

Geometric Tailoring of Macroscale Ti3C2Tx MXene Lamellar Membrane for Logic Gate Circuits

Constructing nanofluidic diode nanochannels with an asymmetric structure for logic gate circuits has attracted extensive research interests. Currently, the preparation of a geometrically asymmetric nanochannel relies on cost-effective material-processing methods and has been hard to scale up, limiting the development of nanofluidic research. Herein, we introduce the idea of geometric tailoring to cut the MXene lamellar membrane in different shapes and investigate the ion transport behavior systematically. The ion rectification can be regulated by adjusting geometric factors such as the asymmetric ratio and height of the trapezoidal membrane. On the basis of the above-mentioned research on rectification characteristics, we further optimized the trapezoidal membrane into a triangular membrane on the macroscopic level and successfully applied it to logic circuits, realizing the logic operations of "AND" and "OR". It is worth mentioning that the shape of a macrocut triangular membrane is exactly the same as the symbol of an electronic diode, and the conduction and cutoff directions of the ionic current are also exactly the same as those of electronic diodes. Our finding provides a facile and general strategy for fabricating a macroscale geometric asymmetry nanochannel-based two-dimensional lamellar membrane and shows the potential applications in complex highly integrated ionic circuits.