Integrated Memory Devices Based on 2D Materials

Transfer of 2D Films: From Imperfection to Perfection

Atomically thin 2D films and their van der Waals heterostructures have demonstrated immense potential for breakthroughs and innovations in science and technology. Integrating 2D films into electronics and optoelectronics devices and their applications in electronics and optoelectronics can lead to improve device efficiencies and tunability. Consequently, there has been steady progress in large-area 2D films for both front- and back-end technologies, with a keen interest in optimizing different growth and synthetic techniques. Parallelly, a significant amount of attention has been directed toward efficient transfer techniques of 2D films on different substrates. Current methods for synthesizing 2D films often involve high-temperature synthesis, precursors, and growth stimulants with highly chemical reactivity. This limitation hinders the widespread applications of 2D films. As a result, reports concerning transfer strategies of 2D films from bare substrates to target substrates have proliferated, showcasing varying degrees of cleanliness, surface damage, and material uniformity. This review aims to evaluate, discuss, and provide an overview of the most advanced transfer methods to date, encompassing wet, dry, and quasi-dry transfer methods. The processes, mechanisms, and pros and cons of each transfer method are critically summarized. Furthermore, we discuss the feasibility of these 2D film transfer methods, concerning their applications in devices and various technology platforms.

Above-room-temperature chiral skyrmion lattice and Dzyaloshinskii-Moriya interaction in a van der Waals ferromagnet Fe3-xGaTe2

Skyrmions in existing 2D van der Waals (vdW) materials have primarily been limited to cryogenic temperatures, and the underlying physical mechanism of the Dzyaloshinskii-Moriya interaction (DMI), a crucial ingredient for stabilizing chiral skyrmions, remains inadequately explored. Here, we report the observation of Néel-type skyrmions in a vdW ferromagnet Fe3-xGaTe2 above room temperature. Contrary to previous assumptions of centrosymmetry in Fe3-xGaTe2, the atomic-resolution scanning transmission electron microscopy reveals that the off-centered FeΙΙ atoms break the spatial inversion symmetry, rendering it a polar metal. First-principles calculations further elucidate that the DMI primarily stems from the Te sublayers through the Fert-Lévy mechanism. Remarkably, the chiral skyrmion lattice in Fe3-xGaTe2 can persist up to 330 K at zero magnetic field, demonstrating superior thermal stability compared to other known skyrmion vdW magnets. This work provides valuable insights into skyrmionics and presents promising prospects for 2D material-based skyrmion devices operating beyond room temperature.

Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing

Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.

Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic Hardware

Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3 ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102 ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.

Highly Reliable Van Der Waals Memory Boosted by a Single 2D Charge Trap Medium

Charge trap materials that can store carriers efficiently and controllably are desired for memory applications. 2D materials are promising for highly compacted and reliable memory mainly due to their ease of constructing atomically uniform interfaces, however, remain unexplored as being charge trap media. Here it is discovered that 2D semiconducting PbI2 is an excellent charge trap material for nonvolatile memory and artificial synapses. It is simple to construct PbI2 -based charge trap devices since no complicated synthesis or additional defect manufacturing are required. As a demonstration, MoS2 /PbI2 device exhibits a large memory window of 120 V, fast write speed of 5 µs, high on-off ratio around 106 , multilevel memory of over 8 distinct states, high reliability with endurance up to 104 cycles and retention over 1.2 × 104 s. It is envisioned that PbI2 with ionic activity caused by the natively formed iodine vacancies is unique to combine with unlimited 2D materials for versatile van der Waals devices with high-integration and multifunctionality.

Moiré synaptic transistor with room-temperature neuromorphic functionality

Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.

High-performance van der Waals antiferroelectric CuCrP2S6-based memristors

Layered thio- and seleno-phosphate ferroelectrics, such as CuInP2S6, are promising building blocks for next-generation nonvolatile memory devices. However, because of the low Curie point, the CuInP2S6-based memory devices suffer from poor thermal stability (<42 °C). Here, exploiting the electric field-driven phase transition in the rarely studied antiferroelectric CuCrP2S6 crystals, we develop a nonvolatile memristor showing a sizable resistive-switching ratio of ~ 1000, high switching endurance up to 20,000 cycles, low cycle-to-cycle variation, and robust thermal stability up to 120 °C. The resistive switching is attributed to the ferroelectric polarization-modulated thermal emission accompanied by the Fowler-Nordheim tunneling across the interfaces. First-principles calculations reveal that the good device performances are associated with the exceptionally strong ferroelectric polarization in CuCrP2S6 crystal. Furthermore, the typical biological synaptic learning rules, such as long-term potentiation/depression and spike amplitude/spike time-dependent plasticity, are also demonstrated. The results highlight the great application potential of van der Waals antiferroelectrics in high-performance synaptic devices for neuromorphic computing.

A review on advanced band-structure engineering with dynamic control for nonvolatile memory based 2D transistors

With advancements in information technology, an enormous amount of data is being generated that must be quickly accessible. However, conventional Si memory cells are approaching their physical limits and will be unable to meet the requirements of intense applications in the future. Notably, 2D atomically thin materials have demonstrated multiple novel physical and chemical properties that can be used to investigate next-generation electronic devices and breakthrough physical limits to continue Moore's law. Band structure is an important semiconductor parameter that determines their electrical and optical properties. In particular, 2D materials have highly tunable bandgaps and Fermi levels that can be achieved through band structure engineering methods such as heterostructure, substrate engineering, chemical doping, intercalation, and electrostatic doping. In particular, dynamic control of band structure engineering can be used in recent advancements in 2D devices to realize nonvolatile storage performance. This study examines recent advancements in 2D memory devices that utilize band structure engineering. The operational mechanisms and memory characteristics are described for each band structure engineering method. Band structure engineering provides a platform for developing new structures and realizing superior performance with respect to nonvolatile memory.

Probing charge traps at the 2D semiconductor/dielectric interface

The family of 2-dimensional (2D) semiconductors is a subject of intensive scientific research due to their potential in next-generation electronics. While offering many unique properties like atomic thickness and chemically inert surfaces, the integration of 2D semiconductors with conventional dielectric materials is challenging. The charge traps at the semiconductor/dielectric interface are among many issues to be addressed before these materials can be of industrial relevance. Conventional electrical characterization methods remain inadequate to quantify the traps at the 2D semiconductor/dielectric interface since the estimations of the density of interface traps, Dit, by different techniques may yield more than an order-of-magnitude discrepancy, even when extracted from the same device. Therefore, the challenge to quantify Dit at the 2D semiconductor/dielectric interface is about finding an accurate and reliable measurement method. In this review, we discuss characterization techniques which have been used to study the 2D semiconductor/dielectric interface. Specifically, we discuss the methods based on small-signal AC measurements, subthreshold slope measurements and low-frequency noise measurements. While these approaches were developed for silicon-based technology, 2D semiconductor devices possess a set of unique challenges requiring a careful re-evaluation when using these characterization techniques. We examine the conventional methods based on their efficacy and accuracy in differentiating various types of trap states and provide guidance to find an appropriate method for charge trap analysis and estimation of Dit at 2D semiconductor/dielectric interfaces.

A Dual-Functional Perovskite-Based Photodetector and Memristor for Visual Memory

The imitation of human visual memory demands the multifunctional integration of light sensors similar to the eyes, and image memory, similar to the brain. Although humans have already implemented electronic devices with visual memory functions, these devices require a combination of various components and logical circuits. However, the combination of visual perception and high-performance information storage capabilities into a single device to achieve visual memory remains challenging. In this study, inspired by the function of human visual memory, a dual-functional perovskite-based photodetector (PD) and memristor are designed to realize visual perception and memory capacities. As a PD, it realizes an ultrahigh self-powered responsivity of 276 mA W-1 , a high detectivity of 4.7 × 1011  Jones (530 nm; light intensities, 2.34 mW cm-2 ), and a high rectification ratio of ≈100 (±2 V). As a memristor, an ultrahigh on/off ratio (≈105 ), an ultralow power consumption of 3 × 10-11  W, a low setting voltage (0.15 V), and a long retention time (>7000 s) are realized. Moreover, the dual-functional device has the capacity to perceive and remember light paths and store data with good cyclic stability. This device exhibits perceptual and cyclic erasable memory functions, which provides new opportunities for mimicking human visual memory in future multifunctional applications.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

A Two-Dimensional Hybrid Lead Bromide Ferroelectric Semiconductor with an Out-of-Plane Polarization

A two-dimensional (2D) organic-inorganic hybrid perovskite (OIHP) material with out-of-plane ferroelectricity is the key to the miniaturization of vertical-sandwich-type ferroelectric optoelectronic devices. However, 2D OIHP ferroelectrics with out-of-plane polarization are still scarce, and effective design strategies are lacking. Herein, we report a novel 2D Dion-Jacobson perovskite ferroelectric semiconductor synthesized by a rigid-to-flexible cationic tailoring strategy, achieving an out-of-plane polarization of 1.7 μC/cm2 and high photoresponse. Integrating out-of-plane ferroelectricity with excellent photoelectric properties affords a promising platform to investigate ferroelectricity-related effects in vertical optoelectronic devices.

Non-volatile resistive switching mechanism in single-layer MoS2 memristors: insights from ab initio modelling of Au and MoS2 interfaces

Non-volatile memristive devices based on two-dimensional (2D) layered materials provide an attractive alternative to conventional flash memory chips. Single-layer semiconductors, such as monolayer molybdenum disulphide (ML-MoS2), enable the aggressive downscaling of devices towards greater system integration density. The "atomristor", the most compact device to date, has been shown to undergo a resistive switching between its high-resistance (HRS) and low-resistance (LRS) states of several orders of magnitude. The main hypothesis behind its working mechanism relies on the migration of sulphur vacancies in the proximity of the metal contact during device operation, thus inducing the variation of the Schottky barrier at the metal-semiconductor interface. However, the interface physics is not yet fully understood: other hypotheses were proposed, involving the migration of metal atoms from the electrode. In this work, we aim to elucidate the mechanism of the resistive switching in the atomristor. We carry out density functional theory (DFT) simulations on model Au and ML-MoS2 interfaces with and without the presence of point defects, either vacancies or substitutions. To construct realistic interfaces, we combine DFT with Green's function surface simulations. Our findings reveal that it is not the mere presence of S vacancies but rather the migration of Au atoms from the electrode to MoS2 that modulate the interface barrier. Indeed, Au atoms act as conductive "bridges", thus facilitating the flow of charge between the two materials.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Two-Dimensional Layered Materials Meet Perovskite Oxides: A Combination for High-Performance Electronic Devices

As the Si-based transistors scale down to atomic dimensions, the basic principle of current electronics, which heavily relies on the tunable charge degree of freedom, faces increasing challenges to meet the future requirements of speed, switching energy, heat dissipation, and packing density as well as functionalities. Heterogeneous integration, where dissimilar layers of materials and functionalities are unrestrictedly stacked at an atomic scale, is appealing for next-generation electronics, such as multifunctional, neuromorphic, spintronic, and ultralow-power devices, because it unlocks technologically useful interfaces of distinct functionalities. Recently, the combination of functional perovskite oxides and two-dimensional layered materials (2DLMs) led to unexpected functionalities and enhanced device performance. In this paper, we review the recent progress of the heterogeneous integration of perovskite oxides and 2DLMs from the perspectives of fabrication and interfacial properties, electronic applications, and challenges as well as outlooks. In particular, we focus on three types of attractive applications, namely field-effect transistors, memory, and neuromorphic electronics. The van der Waals integration approach is extendible to other oxides and 2DLMs, leading to almost unlimited combinations of oxides and 2DLMs and contributing to future high-performance electronic and spintronic devices.

Electrical Contacts With 2D Materials: Current Developments and Future Prospects

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.