Low-Power Memristive Logic Device Enabled by Controllable Oxidation of 2D HfSe2 for In-Memory Computing

Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing

Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.

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.

Low-Temperature Nanosecond Laser Process of HZO-IGZO FeFETs toward Monolithic 3D System on Chip Integration

Ferroelectric field-effect transistors (FeFETs) are increasingly important for in-memory computing and monolithic 3D (M3D) integration in system-on-chip (SoC) applications. However, the high-temperature processing required by most ferroelectric memories can lead to thermal damage to the underlying device layers, which poses significant physical limitations for 3D integration processes. To solve this problem, the study proposes using a nanosecond pulsed laser for selective annealing of hafnia-based FeFETs, enabling precise control of heat penetration depth within thin films. Sufficient thermal energy is delivered to the IGZO oxide channel and HZO ferroelectric gate oxide without causing thermal damage to the bottom layer, which has a low transition temperature (<250 °C). Using optimized laser conditions, a fast response time (<1 µs) and excellent stability (cycle > 106, retention > 106 s) are achieved in the ferroelectric HZO film. The resulting FeFET exhibited a wide memory window (>1.7 V) with a high on/off ratio (>105). In addition, moderate ferroelectric properties (2·Pr of 14.7 µC cm-2) and pattern recognition rate-based linearity (potentiation: 1.13, depression: 1.6) are obtained. These results demonstrate compatibility in HZO FeFETs by specific laser annealing control and thin-film layer design for various structures (3D integrated, flexible) with neuromorphic applications.

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.

Mild chemistry synthesis of ultrathin Bi2O2S nanosheets exhibiting 2D-ferroelectricity at room temperature

Modern technology demands miniaturization of electronic components to build small, light, and portable devices. Hence, discovery and synthesis of new non-toxic, low cost, ultra-thin ferroelectric materials having potential applications in various electronic and optoelectronic devices are of paramount importance. However, achieving room-temperature ferroelectricity in two dimensional (2D) ultra-thin systems remains a major challenge as conventional three-dimensional ferroelectric materials lose their ferroelectricity when the thickness is brought down below a critical value owing to the depolarization field. Herein, we report room-temperature ferroelectricity in ultra-thin single-crystalline 2D nanosheets of Bi2O2S synthesized by a simple, rapid, and scalable solution-based soft chemistry method. The ferroelectric ground state of Bi2O2S nanosheets is confirmed by temperature-dependent dielectric measurements as well as piezoelectric force microscopy and spectroscopy. High resolution transmission electron microscopy analysis and density functional theory-based calculations suggest that the ferroelectricity in Bi2O2S nanosheets arises due to the local distortion of Bi2O2 layers, which destroys the local inversion symmetry of Bi2O2S.

Ag-doped non-imperfection-enabled uniform memristive neuromorphic device based on van der Waals indium phosphorus sulfide

Memristors are considered promising energy-efficient artificial intelligence hardware, which can eliminate the von Neumann bottleneck by parallel in-memory computing. The common imperfection-enabled memristors are plagued with critical variability issues impeding their commercialization. Reported approaches to reduce the variability usually sacrifice other performances, e.g., small on/off ratios and high operation currents. Here, we demonstrate an unconventional Ag-doped nonimperfection diffusion channel-enabled memristor in van der Waals indium phosphorus sulfide, which can combine ultralow variabilities with desirable metrics. We achieve operation voltage, resistance, and on/off ratio variations down to 3.8, 2.3, and 6.9% at their extreme values of 0.2 V, 1011 ohms, and 108, respectively. Meanwhile, the operation current can be pushed from 1 nA to 1 pA at the scalability limit of 6 nm after Ag doping. Fourteen Boolean logic functions and convolutional image processing are successfully implemented by the memristors, manifesting the potential for logic-in-memory devices and efficient non-von Neumann accelerators.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

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.

Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications

Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiO_x-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiO_x/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (10⁴ s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiO_x-based memristors have great potential for use in multilevel memory and neuromorphic computing systems.

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.

Neotype kuramite optoelectronic memristor for bio-synaptic plasticity simulations

Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼-0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and "learning-forgetting-learning" behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions.

Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe2 Enable Uniform and Fast Memristive Switching

Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe₂ material and then oxidized it into TiO₂ in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe₂/TiO₂/TiSe₂ device exhibits excellent resistive switching performance with a fairly low threshold voltage (V_set < 0.8 V, V_reset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.

Controllable Oxidation of ZrS2 to Prepare High-κ, Single-Crystal m-ZrO2 for 2D Electronics

High-κ materials that exhibit large permittivity and band gaps are needed as gate dielectrics to enhance capacitance and prevent leakage current in downsized technology nodes. Among these, monoclinic ZrO₂ (m-ZrO₂ ) shows good potential because of its inertness and high-κ with respect to SiO₂ , but a method to produce ultrathin single crystal is lacking. Here, the controllable preparation of ultrathin m-ZrO₂ single crystals via the in situ thermal oxidation of ZrS₂ is achieved. As-grown m-ZrO₂ presents an equivalent oxide thickness of ≈0.29 nm, a high dielectric constant of ≈19, and a breakdown voltage (E_BD ) of ≈7.22 MV cm^-1 . MoS₂ field effect transistor (FET) by using m-ZrO₂ as a dielectric layer shows comparable mobility to that using SiO₂ dielectric. The ultraclean interface of m-ZrO₂ /MoS₂ and high crystalline quality of m-ZrO₂ lead to negligible hysteresis in transfer curves. Single crystal m-ZrO₂ dielectric shows potential application in digital complementary metal oxidesemiconductor (CMOS) logic FET.

Ultrafast and Low-Power 2D Bi2O2Se Memristors for Neuromorphic Computing Applications

Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi₂O₂Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi₂O₂Se nanosheets are grown by configuring mica substrates face-down on the Bi₂O₂Se powder. Then, bipolar Bi₂O₂Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi₂O₂Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi₂O₂Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.

Van der Waals Ferroelectric Semiconductor Field Effect Transistor for In-Memory Computing

In-memory computing is a highly efficient approach for breaking the bottleneck of von Neumann architectures, i.e., reducing redundant latency and energy consumption during the data transfer between the physically separated memory and processing units. Herein we have designed a in-memory computing device, a van der Waals ferroelectric semiconductor (InSe) based metal-oxide-ferroelectric semiconductor field-effect transistor (MOfeS-FET). This MOfeS-FET integrates memory and logic functions in the same material, in which the out-of-plane (OOP) ferroelectric polarization in InSe is used for data storage and the semiconducting property is used for the logic computation. The MOfeS-FET shows a long retention time with high on/off ratios (>10⁶), high program/erase (P/E) ratios (10³), and stable cyclic endurance. Moreover, inverter, programmable NAND, and NOR Boolean logic operations with nonvolatile storage of the results have all been demonstrated using our approach. These findings highlight the potential of van der Waals ferroelectric semiconductor-based MOfeS-FETs in the in-memory computing and their potential of achieving size scaling beyond Moore's law.

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.

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.

Morphotaxy of Layered van der Waals Materials

Layered van der Waals (vdW) materials have attracted significant attention due to their materials properties that can enhance diverse applications including next-generation computing, biomedical devices, and energy conversion and storage technologies. This class of materials is typically studied in the two-dimensional (2D) limit by growing them directly on bulk substrates or exfoliating them from parent layered crystals to obtain single or few layers that preserve the original bonding. However, these vdW materials can also function as a platform for obtaining additional phases of matter at the nanoscale. Here, we introduce and review a synthesis paradigm, morphotaxy, where low-dimensional materials are realized by using the shape of an initial nanoscale precursor to template growth or chemical conversion. Using morphotaxy, diverse non-vdW materials such as HfO₂ or InF₃ can be synthesized in ultrathin form by changing the composition but preserving the shape of the original 2D layered material. Morphotaxy can also enable diverse atomically precise heterojunctions and other exotic structures such as Janus materials. Using this morphotaxial approach, the family of low-dimensional materials can be substantially expanded, thus creating vast possibilities for future fundamental studies and applied technologies.

Artificial Synapses Based on WSe2 Homojunction via Vacancy Migration

Artificial synapses based on two-dimensional (2D) transition metal dichalcogenides (TMDs) materials have attracted wide attention to boost the development of neuromorphic computing in recent years. Various structures have been adopted to build 2D-material-based artificial synapses. In lateral- and vertical-structures, the realization of synaptic function mainly results from the migration of the defects and vacancies, which requires the strong ion diffusion ability. Here, we successfully demonstrate an artificial synapse based on lateral WSe₂ homojunction. The migration of Se vacancies from the thin region to the thick region has been promoted by applying negative gate voltage, resulting in n-type doping in the thick region due to the accumulation of Se vacancies, which would diminish the barrier width of the metal-semiconductor junctions in the thick region. Consequently, the transformation from a high-resistance state (HRS) to a low-resistance state (LRS) is achieved. Significantly, our device can efficiently emulate the biological synaptic functions with a large synaptic weight change. Additionally, the transition from short-term memory (STM) to long-term memory (LTM) can be accomplished with a simpler structure, which would be beneficial to realizing the large-scale integration of transistor-based artificial synapses.

Nanostructured perovskites for nonvolatile memory devices

Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.

Ternary Logic with Stateful Neural Networks Using a Bilayered TaOX -Based Memristor Exhibiting Ternary States

A memristive stateful neural network allowing complete Boolean in-memory computing attracts high interest in future electronics. Various Boolean logic gates and functions demonstrated so far confirm their practical potential as an emerging computing device. However, spatio-temporal efficiency of the stateful logic is still too limited to replace conventional computing technologies. This study proposes a ternary-state memristor device (simply a ternary memristor) for application to ternary stateful logic. The ternary-state implementable memristor device is developed with bilayered tantalum oxide by precisely controlling the oxygen content in each oxide layer. The device can operate 157 ternary logic gates in one operational clock, which allows an experimental demonstration of a functionally complete three-valued Łukasiewicz logic system. An optimized logic cascading strategy with possible ternary gates is ≈20% more efficient than conventional binary stateful logic, suggesting it can be beneficial for higher performance in-memory computing.