Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

Ultrafast high-endurance memory based on sliding ferroelectrics

The persistence of voltage-switchable collective electronic phenomena down to the atomic scale has extensive implications for area- and energy-efficient electronics, especially in emerging nonvolatile memory technology. We investigate the performance of a ferroelectric field-effect transistor (FeFET) based on sliding ferroelectricity in bilayer boron nitride at room temperature. Sliding ferroelectricity represents a different form of atomically thin two-dimensional (2D) ferroelectrics, characterized by the switching of out-of-plane polarization through interlayer sliding motion. We examined the FeFET device employing monolayer graphene as the channel layer, which demonstrated ultrafast switching speeds on the nanosecond scale and high endurance exceeding 1011 switching cycles, comparable to state-of-the-art FeFET devices. These characteristics highlight the potential of 2D sliding ferroelectrics for inspiring next-generation nonvolatile memory technology.

Developing fatigue-resistant ferroelectrics using interlayer sliding switching

Ferroelectric materials have switchable electrical polarization that is appealing for high-density nonvolatile memories. However, inevitable fatigue hinders practical applications of these materials. Fatigue-free ferroelectric switching could dramatically improve the endurance of such devices. We report a fatigue-free ferroelectric system based on the sliding ferroelectricity of bilayer 3R molybdenum disulfide (3R-MoS2). The memory performance of this ferroelectric device does not show the wake-up effect at low cycles or a substantial fatigue effect after 106 switching cycles under different pulse widths. The total stress time of the device under an electric field is up to 105 s, which is long relative to other devices. Our theoretical calculations reveal that the fatigue-free feature of sliding ferroelectricity is due to the immobile charge defects in sliding ferroelectricity.

Toward human-like adaptability in robotics through a retention-engineered synaptic control system

Although advanced robots can adeptly mimic human movement and aesthetics, they are still unable to adapt or evolve in response to external experiences. To address this limitation, we propose an innovative approach that uses parallel-processable retention-engineered synaptic devices in the control system. This approach aims to simulate a human-like learning system without necessitating complex computational systems. The retention properties of the synaptic devices were modulated by adjusting the amount of Ag/AgCl ink sprayed. This changed the voltage drop across the interface between the gate electrode and the electrolyte. Furthermore, the unrestricted movement of ions in the electrolyte enhanced the signal multiplexing capability of the ion gel, enabling device-level parallel processing. By integrating the unique characteristics of the synaptic devices with actuators, we successfully emulated a human-like workout process that includes feedback between acute and chronic responses. The proposed control system offers an innovative approach to reducing system complexity and achieving a human-like learning system in the field of biomimicry.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Recent progresses in transmission electron microscopy studies of two-dimensional ferroelectrics

The rich potential of two-dimensional materials endows them with superior properties suitable for a wide range of applications, thereby attracting substantial interest across various fields. The ongoing trend towards device miniaturization aligns with the development of materials at progressively smaller scales, aiming to achieve higher integration density in electronics. In the realm of nano-scaling ferroelectric phenomena, numerous new two-dimensional ferroelectric materials have been predicted theoretically and subsequently validated through experimental confirmation. However, the capabilities of conventional tools, such as electrical measurements, are limited in providing a comprehensive investigation into the intrinsic origins of ferroelectricity and its interactions with structural factors. These factors include stacking, doping, functionalization, and defects. Consequently, the progress of potential applications, such as high-density memory devices, energy conversion systems, sensing technologies, catalysis, and more, is impeded. In this paper, we present a review of recent research that employs advanced transmission electron microscopy (TEM) techniques for the direct visualization and analysis of ferroelectric domains, domain walls, and other crucial features at the atomic level within two-dimensional materials. We discuss the essential interplay between structural characteristics and ferroelectric properties on the nanoscale, which facilitates understanding of the complex relationships governing their behavior. By doing so, we aim to pave the way for future innovative applications in this field.

Scalable Synthesis of High-Quality Ultrathin Ferroelectric Magnesium Molybdenum Oxide

The development of ultrathin, stable ferroelectric materials is crucial for advancing high-density, low-power electronic devices. Nonetheless, ultrathin ferroelectric materials are rare due to the critical size effect. Here, a novel ferroelectric material, magnesium molybdenum oxide (Mg2Mo3O8) is presented. High-quality ultrathin Mg2Mo3O8 crystals are synthesized using chemical vapor deposition (CVD). Ultrathin Mg2Mo3O8 has a wide bandgap (≈4.4 eV) and nonlinear optical response. Mg2Mo3O8 crystals of varying thicknesses exhibit out-of-plane ferroelectric properties at room temperature, with ferroelectricity retained even at a 2 nm thickness. The Mg2Mo3O8 exhibits a relatively large remanent polarization ranging from 33 to 52 µC cm- 2, which is tunable by changing its thickness. Notably, Mg2Mo3O8 possesses a high Curie temperature (>980 °C) across various thicknesses. Moreover, the as-grown Mg2Mo3O8 crystals display remarkable stability under harsh environments. This work introduces nolanites-type crystal into ultrathin ferroelectrics. The scalable synthesis of stable ultrathin ferroelectric Mg2Mo3O8 expands the scope of ferroelectric materials and may prosper applications of ferroelectrics.

Emerging 2D Ferroelectric Devices for In-Sensor and In-Memory Computing

The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics.

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

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

Van der Waals Ferroelectrics: Theories, Materials, and Device Applications

In recent years, an increasing number of 2D van der Waals (vdW) materials are theory-predicted or laboratory-validated to possess in-plane (IP) and/or out-of-plane (OOP) spontaneous ferroelectric polarization. Due to their dangling-bond-free surfaces, interlayer charge coupling, robust polarization, tunable energy band structures, and compatibility with silicon-based technologies, vdW ferroelectric materials exhibit great promise in ferroelectric memories, neuromorphic computing, nanogenerators, photovoltaic devices, spintronic devices, and so on. Here, the very recent advances in the field of vdW ferroelectrics (FEs) are reviewed. First, theories of ferroelectricity are briefly discussed. Then, a comprehensive summary of the non-stacking vdW ferroelectric materials is provided based on their crystal structures and the emerging sliding ferroelectrics. In addition, their potential applications in various branches/frontier fields are enumerated, with a focus on artificial intelligence. Finally, the challenges and development prospects of vdW ferroelectrics are discussed.

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

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

Bevel-edge epitaxy of ferroelectric rhombohedral boron nitride single crystal

Within the family of two-dimensional dielectrics, rhombohedral boron nitride (rBN) is considerably promising owing to having not only the superior properties of hexagonal boron nitride1-4-including low permittivity and dissipation, strong electrical insulation, good chemical stability, high thermal conductivity and atomic flatness without dangling bonds-but also useful optical nonlinearity and interfacial ferroelectricity originating from the broken in-plane and out-of-plane centrosymmetry5-23. However, the preparation of large-sized single-crystal rBN layers remains a challenge24-26, owing to the requisite unprecedented growth controls to coordinate the lattice orientation of each layer and the sliding vector of every interface. Here we report a facile methodology using bevel-edge epitaxy to prepare centimetre-sized single-crystal rBN layers with exact interlayer ABC stacking on a vicinal nickel surface. We realized successful accurate fabrication over a single-crystal nickel substrate with bunched step edges of the terrace facet (100) at the bevel facet (110), which simultaneously guided the consistent boron-nitrogen bond orientation in each BN layer and the rhombohedral stacking of BN layers via nucleation near each bevel facet. The pure rhombohedral phase of the as-grown BN layers was verified, and consequently showed robust, homogeneous and switchable ferroelectricity with a high Curie temperature. Our work provides an effective route for accurate stacking-controlled growth of single-crystal two-dimensional layers and presents a foundation for applicable multifunctional devices based on stacked two-dimensional materials.

Photo-Assisted Ferroelectric Domain Control for α-In2Se3 Artificial Synapses Inspired by Spontaneous Internal Electric Fields

α-In2Se3 semiconductor crystals realize artificial synapses by tuning in-plane and out-of-plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α-In2Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α-In2Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α-In2Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α-In2Se3/hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built-in electric field on synapse characterization. The biased dipoles in α-In2Se3 toward potentiation polarization direction by an enhanced internal built-in electric field under illumination of white light lead to improvement of linearity for long-term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α-In2Se3/hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand-written digit number images.

Coupled Ferroelectric-Photonic Memory in a Retinomorphic Hardware for In-Sensor Computing

The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system

The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Non-volatile electrical polarization switching via domain wall release in 3R-MoS2 bilayer

Understanding the nature of sliding ferroelectricity is of fundamental importance for the discovery and application of two-dimensional ferroelectric materials. In this work, we investigate the phenomenon of switchable polarization in a bilayer MoS2 with natural rhombohedral stacking, where the spontaneous polarization is coupled with excitonic effects through asymmetric interlayer coupling. Using optical spectroscopy and imaging techniques, we observe how a released domain wall switches the polarization of a large single domain. Our results highlight the importance of domain walls in the polarization switching of non-twisted rhombohedral transition metal dichalcogenides and open new opportunities for the non-volatile control of their optical response.

Neuromorphic electro-stimulation based on atomically thin semiconductor for damage-free inflammation inhibition

Inflammation, caused by accumulation of inflammatory cytokines from immunocytes, is prevalent in a variety of diseases. Electro-stimulation emerges as a promising candidate for inflammatory inhibition. Although electroacupuncture is free from surgical injury, it faces the challenges of imprecise pathways/current spikes, and insufficiently defined mechanisms, while non-optimal pathway or spike would require high current amplitude, which makes electro-stimulation usually accompanied by damage and complications. Here, we propose a neuromorphic electro-stimulation based on atomically thin semiconductor floating-gate memory interdigital circuit. Direct stimulation is achieved by wrapping sympathetic chain with flexible electrodes and floating-gate memory are programmable to fire bionic spikes, thus minimizing nerve damage. A substantial decrease (73.5%) in inflammatory cytokine IL-6 occurred, which also enabled better efficacy than commercial stimulator at record-low currents with damage-free to sympathetic neurons. Additionally, using transgenic mice, the anti-inflammation effect is determined by β2 adrenergic signaling from myeloid cell lineage (monocytes/macrophages and granulocytes).

Characteristic Plasmon Energies for 2D In2Se3 Phase Identification at Nanoscale

Two-dimensional (2D) materials with competing polymorphs offer remarkable potential to switch the associated 2D functionalities for novel device applications. Probing their phase transition and competition mechanisms requires nanoscale characterization techniques that can sensitively detect the nucleation of secondary phases down to single-layer thickness. Here we demonstrate nanoscale phase identification on 2D In2Se3 polymorphs, utilizing their distinct plasmon energies that can be distinguished by electron energy-loss spectroscopy (EELS). The characteristic plasmon energies of In2Se3 polymorphs have been validated by first-principles calculations, and also been successfully applied to reveal phase transitions using in situ EELS. Correlating with in situ X-ray diffraction, we further derive a subtle difference in the valence electron density of In2Se3 polymorphs, consistent with their disparate electronic properties. The nanometer resolution and independence of orientation make plasmon-energy mapping a versatile technique for nanoscale phase identification on 2D materials.

Asymmetric conducting route and potential redistribution determine the polarization-dependent conductivity in layered ferroelectrics

Precise control of the conductivity of layered ferroelectric semiconductors is required to make these materials suitable for advanced transistor, memory and logic circuits. Although proof-of-principle devices based on layered ferroelectrics have been demonstrated, it remains unclear how the polarization inversion induces conductivity changes. Therefore, function design and performance optimization remain cumbersome. Here we combine ab initio calculations with transport experiments to unveil the mechanism underlying the polarization-dependent conductivity in ferroelectric channel field-effect transistors. We find that the built-in electric field gives rise to an asymmetric conducting route formed by the hidden Stark effect and competes with the potential redistribution caused by the external field of the gate. Furthermore, leveraging our mechanistic findings, we control the conductivity threshold in α-In2Se3 ferroelectric channel field-effect transistors. We demonstrate logic-in-memory functionality through the implementation of electrically self-switchable primary (AND, OR) and composite (XOR, NOR, NAND) logic gates. Our work provides mechanistic insights into conductivity modulation in a broad class of layered ferroelectrics, providing foundations for their application in logic and memory electronics.

High-throughput computational stacking reveals emergent properties in natural van der Waals bilayers

Stacking of two-dimensional (2D) materials has emerged as a facile strategy for realising exotic quantum states of matter and engineering electronic properties. Yet, developments beyond the proof-of-principle level are impeded by the vast size of the configuration space defined by layer combinations and stacking orders. Here we employ a density functional theory (DFT) workflow to calculate interlayer binding energies of 8451 homobilayers created by stacking 1052 different monolayers in various configurations. Analysis of the stacking orders in 247 experimentally known van der Waals crystals is used to validate the workflow and determine the criteria for realisable bilayers. For the 2586 most stable bilayer systems, we calculate a range of electronic, magnetic, and vibrational properties, and explore general trends and anomalies. We identify an abundance of bistable bilayers with stacking order-dependent magnetic or electrical polarisation states making them candidates for slidetronics applications.

A ferroelectric fin diode for robust non-volatile memory

Among today's nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 1010 cycles, an ON/OFF ratio of ~102, a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 104 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing.

Large-Area Growth of Ferroelectric 2D γ-In2 Se3 Semiconductor by Spray Pyrolysis for Next-Generation Memory

In2 Se3 , 2D ferroelectric-semiconductor, is a promising candidate for next-generation memory device because of its outstanding electrical properties. However, the large-area manufacturing of In2 Se3 is still a big challenge. In this work, spray pyrolysis technique is introduced for the growth of large-area In2 Se3 thin film. A polycrystalline γ-In2 Se3 layer can be grown on 15 cm × 15 cm glasss at the substrate temperature of 275 °C. The In2 Se3 ferroelectric-semiconductor field effect transistor (FeS-FET) on glass substrate demonstrates a large hysteresis window of 40.3 V at the ±40 V of gate voltage sweep and excellent uniformity. The FeS-FET exhibits an electron field effect mobility of 0.97 cm2 V-1 s-1 and an on/off current ratio of >107 in the transfer curves. The memory behavior of the large-area, In2 Se3 FeS-FETs for next-generation memory is demonstrated.

Ferroelectric Organic Semiconductor: [1]Benzothieno[3,2-b][1]benzothiophene-Bearing Hydrogen-Bonding -CONHC14H29 Chain

An alkylamide-substituted [1]benzothieno[3,2-b][1]benzothiophene (BTBT) derivative of BTBT-CONHC14H29 (1) and C8H17-BTBT-CONHC14H29 (2) were prepared to design the multifunctional organic materials, which can show both ferroelectric and semiconducting properties. Single-crystal X-ray structural analyses of short-chain (-CONHC3H7) derivatives revealed the coexistence of two-dimensional (2D) electronic band structures brought from a herringbone arrangement of the BTBT π core and the one-dimensional (1D) hydrogen-bonding chains of -CONHC3H7 chains. The thin films of 1 and 2 fabricated on the Si/SiO2 substrate surface have monolayer and bilayer structures, respectively, resulting in conducting layers parallel to the substrate surface, which is suitable for a channel layer of organic field-effect transistors (OFETs). The thin film of 1 indicated a hole mobility μFET = 2.4 × 10-5 cm2 V-1 s-1 and threshold voltage VTh = - 29 V, whereas that of 2 showed a μFET = 2.1 × 10-2 cm2 V-1 s-1 and threshold voltage VTh = -9.7 V. Both 1 and 2 formed the smectic E (SmE) phase above 410 and 369 K, respectively, where the existence of a hole transport pathway was confirmed in the SmE phase. The ferroelectric hysteresis behavior was observed in bulk 1 and 2 in the polarization-electric field (P-E) curves at the SmE phase. 1 showed the remanent polarization Pr = 2.3 μC cm-2 and coercive electric field Ec = 5.2 V μm-1, whereas the Pr and Ec of 2 were 3.4 μC cm-2 and 7.0 V μm-1 at the conditions of 453 K and 1 Hz. Introduction of alkylamide units into the BTBT π core has the potential to develop the external stimulus-responsive organic semiconductors brought from both ferroelectricity and semiconducting properties.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

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.

Chemical Modulation of Metal-Insulator Transition toward Multifunctional Applications in Vanadium Dioxide Nanostructures

The metal-insulator transition (MIT) of vanadium dioxide (VO2 ) has been of great interest in materials science for both fundamental understanding of strongly correlated physics and a wide range of applications in optics, thermotics, spintronics, and electronics. Due to the merits of chemical interaction with accessibility, versatility, and tunability, chemical modification provides a new perspective to regulate the MIT of VO2 , endowing VO2 with exciting properties and improved functionalities. In the past few years, plenty of efforts have been devoted to exploring innovative chemical approaches for the synthesis and MIT modulation of VO2 nanostructures, greatly contributing to the understanding of electronic correlations and development of MIT-driven functionalities. Here, this comprehensive review summarizes the recent achievements in chemical synthesis of VO2 and its MIT modulation involving hydrogen incorporation, composition engineering, surface modification, and electrochemical gating. The newly appearing phenomena, mechanism of electronic correlation, and structural instability are discussed. Furthermore, progresses related to MIT-driven applications are presented, such as the smart window, optoelectronic detector, thermal microactuator, thermal radiation coating, spintronic device, memristive, and neuromorphic device. Finally, the challenges and prospects in future research of chemical modulation and functional applications of VO2 MIT are also provided.

Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition

Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses and neurons will facilitate integration density and computility. Two-dimensional (2D) materials exhibit potential for artificial synapses, including diverse biomimetic plasticity and efficient computing. Considering the complexity of neuron circuits and the maturity of complementary metal-oxide-semiconductor (CMOS), hybrid integration is attractive. Here, we demonstrate a hybrid neuromorphic hardware with 2D MoS2 synaptic arrays and CMOS neural circuitry integrated on board. With the joint benefit of hybrid integration, frequency coding and feature extraction, a total cost of twelve MoS2 synapses, three CMOS neurons, combined with digital/analogue converter enables alphabetic and numeric recognition. MoS2 synapses exhibit progressively tunable weight plasticity, CMOS neurons integrate and fire frequency-encoded spikes to display the target characters. The synapse- and neuron-saving hybrid hardware exhibits a competitive accuracy of 98.8% and single recognition energy consumption of 11.4 μW. This work provides a viable solution for building neuromorphic hardware with high compactness and computility.

Laterally gated ferroelectric field effect transistor (LG-FeFET) using α-In2Se3 for stacked in-memory computing array

In-memory computing is an attractive alternative for handling data-intensive tasks as it employs parallel processing without the need for data transfer. Nevertheless, it necessitates a high-density memory array to effectively manage large data volumes. Here, we present a stacked ferroelectric memory array comprised of laterally gated ferroelectric field-effect transistors (LG-FeFETs). The interlocking effect of the α-In2Se3 is utilized to regulate the channel conductance. Our study examined the distinctive characteristics of the LG-FeFET, such as a notably wide memory window, effective ferroelectric switching, long retention time (over 3 × 104 seconds), and high endurance (over 105 cycles). This device is also well-suited for implementing vertically stacked structures because decreasing its height can help mitigate the challenges associated with the integration process. We devised a 3D stacked structure using the LG-FeFET and verified its feasibility by performing multiply-accumulate (MAC) operations in a two-tier stacked memory configuration.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.

Bottom Contact 100 nm Channel-Length α-In2 Se3 In-Plane Ferroelectric Memory

Owing to the emerging trend of non-volatile memory and data-centric computing, the demand for more functional materials and efficient device architecture at the nanoscale is becoming stringent. To date, 2D ferroelectrics are cultivated as channel materials in field-effect transistors for their retentive and switchable dipoles and flexibility to be compacted into diverse structures and integration for intensive production. This study demonstrates the in-plane (IP) ferroelectric memory effect of a 100 nm channel-length 2D ferroelectric semiconductor α-In2 Se3 stamped onto nanogap electrodes on Si/SiO2 under a lateral electric field. As α-In2 Se3 forms the bottom contact of the nanogap electrodes, a large memory window of 13 V at drain voltage between ±6.5 V and the on/off ratio reaching 103 can be explained by controlled IP polarization. Furthermore, the memory effect is modulated by the bottom gate voltage of the Si substrate due to the intercorrelation between IP and out-of-plane (OOP) polarization. The non-volatile memory characteristics including stable retention lasting 17 h, and endurance over 1200 cycles suggest a wide range of memory applications utilizing the lateral bottom contact structure.

Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision

Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.

Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material

Currently, for most three-terminal neuromorphic devices, only the gate terminal is active. The inadequate modes and freedom of modulation in such devices greatly hinder the implementation of complex neural behaviors and brain-like thinking strategies in hardware systems. Taking advantage of the unique feature of co-existing in-plane (IP) and out-of-plane (OOP) ferroelectricity in two-dimensional (2D) ferroelectric α-In2Se3, we construct a three-active-terminal neuromorphic device where any terminal can modulate the conductance state. Based on the co-operation mode, controlling food intake as a complex nervous system-level behavior is achieved to carry out positive and negative feedback. Specifically, reinforcement learning as a brain-like thinking strategy is implemented due to the coupling between polarizations in different directions. Compared to the single modulation mode, the chance of the agent successfully obtaining the reward in the Markov decision process is increased from 68% to 82% under the co-operation mode through the coupling effect between IP and OOP ferroelectricity in 2D α-In2Se3 layers. Our work demonstrates the practicability of three-active-terminal neuromorphic devices in handling complex tasks and advances a significant step towards implementing brain-like learning strategies based on neuromorphic devices for dealing with real-world challenges.

Flexible, Transparent, and Wafer-Scale Artificial Synapse Array Based on TiOx /Ti3 C2 Tx Film for Neuromorphic Computing

A high-density neuromorphic computing memristor array based on 2D materials paves the way for next-generation information-processing components and in-memory computing systems. However, the traditional 2D-materials-based memristor devices suffer from poor flexibility and opacity, which hinders the application of memristors in flexible electronics. Here, a flexible artificial synapse array based on TiOx /Ti3 C2 Tx film is fabricated by a convenient and energy-efficient solution-processing technique, which realizes high transmittance (≈90%) and oxidation resistance (>30 days). The TiOx /Ti3 C2 Tx memristor shows low device-to-device variability, long memory retention and endurance, a high ON/OFF ratio, and fundamental synaptic behavior. Furthermore, satisfactory flexibility (R = 1.0 mm) and mechanical endurance (104 bending cycles) of the TiOx /Ti3 C2 Tx memristor are achieved, which is superior to other film memristors prepared by chemical vapor deposition. In addition, high-precision (>96.44%) MNIST handwritten digits recognition classification simulation indicates that the TiOx /Ti3 C2 Tx artificial synapse array holds promise for future neuromorphic computing applications, and provides excellent high-density neuron circuits for new flexible intelligent electronic equipment.

Reversible Diode with Tunable Band Alignment for Photoelectricity-Induced Artificial Synapse

The advent of big data era has put forward higher requirements for electronic nanodevices that have low energy consumption for their application in analog computing with memory and logic circuit to address attendant energy efficiency issues. Here, a miniaturized diode with a reversible switching state based on N-n MoS2 homojunction used a bandgap renormalization effect through the band alignment type regulated by both dielectric and polarization, controllably switched between type-I and type-II, which can be simulated as artificial synapse for sensing memory processing because of its rectification, nonvolatile characteristic and high optical responsiveness. The device demonstrates a rectification ratio of 103 . When served as memory retention time, it can attain at least 7000 s. For the synapse simulation, it has an ultralow-level energy consumption because of the pA-level operation current with 5 pJ for long-term potentiation and 7.8 fJ for long-term depression. Furthermore, the paired pulse facilitation index reaches up to 230%, and it realizes the function of optical storage that can be applied to simulate visual cells.

A Light-Programmed Rewritable Lattice-Mediated Multistate Memory for High-Density Data Storage

Mainstream non-volatile memory (NVM) devices based on floating gate structures or phase-change/ferroelectric materials face inherent limitations that compromise their suitability for long-term data storage. To address this challenge, a novel memory device based on light-programmed lattice engineering of thin rhenium disulfide (ReS2 ) flakes is proposed. By inducing sulfur vacancies in the ReS2 channel through light illumination, the device's electrical conductivity is modified accordingly and multiple conductance states for data storage therefore are generated. The device exhibits more than 128 distinct states with linearly increasing conductance, corresponding to a sevenfold increase in storage density. Through further optimization to achieve atomic-level precision in defect creation, it is possible to achieve even higher storage densities. These states are extremely stable in vacuum or inert ambient showing long retention of >10 years, while they can be erased upon exposure to the air. The ReS2 memory device can maintain its stability over multiple program-erase operation cycles and shows superior wavelength discrimination capability for incident light in the range of 405-785 nm. This device represents a significant contribution to NVM technology by offering the ability to store information in multistate memory and enabling filter-free color image recorder applications.

Integration of image preprocessing and recognition functions in an optoelectronic coupling organic ferroelectric retinomorphic neuristor

The human visual system (HVS) has the advantages of a low power consumption and high efficiency because of the synchronous perception and early preprocessing of external image information in the retina, as well as parallel in-memory computing within the visual cortex. Realizing the biofunction simulation of the retina and visual cortex in a single device structure provides opportunities for performance improvements and machine vision system (MVS) integration. Here, we fabricate organic ferroelectric retinomorphic neuristors that integrate the retina-like preprocessing function and recognition of the visual cortex in a single device architecture. Benefiting from the electrical/optical coupling modulation of ferroelectric polarization, our devices show a bidirectional photoresponse that acts as the basis for mimicking retinal preconditioning and multi-level memory capabilities for recognition. The MVS based on the proposed retinomorphic neuristors achieves a high recognition accuracy of ∼90%, which is 20% higher than that of the incomplete system without the preprocessing function. In addition, we successfully demonstrate image encryption and optical programming logic gate functions. Our work suggests that the proposed retinomorphic neuristors offer great potential for MVS monolithic integration and functional expansion.

2D Ferroic Materials for Nonvolatile Memory Applications

The emerging nonvolatile memory technologies based on ferroic materials are promising for producing high-speed, low-power, and high-density memory in the field of integrated circuits. Long-range ferroic orders observed in 2D materials have triggered extensive research interest in 2D magnets, 2D ferroelectrics, 2D multiferroics, and their device applications. Devices based on 2D ferroic materials and heterostructures with an atomically smooth interface and ultrathin thickness have exhibited impressive properties and significant potential for developing advanced nonvolatile memory. In this context, a systematic review of emergent 2D ferroic materials is conducted here, emphasizing their recent research on nonvolatile memory applications, with a view to proposing brighter prospects for 2D magnetic materials, 2D ferroelectric materials, 2D multiferroic materials, and their relevant devices.

Giant tunneling electroresistance in a 2D bilayer-In2Se3-based out-of-plane ferroelectric tunnel junction

Ferroelectric tunnel junctions (FTJs) have great potential in nonvolatile memory devices and have been extensively studied in recent years. Compared with conventional FTJs based on perovskite-type oxide materials as the barrier layer, two-dimensional (2D) van der Waals ferroelectric materials are advantageous in improving the performance of FTJs and achieving miniaturization of FTJ devices due to the features such as atomic thickness and ideal interfaces. In this work, we present a 2D out-of-plane ferroelectric tunnel junction (FTJ) constructed using graphene and bilayer-In₂Se₃. Using density functional calculations combined with the nonequilibrium Green's function technique, we investigate the electron transport properties in the graphene/bilayer-In₂Se₃ (BIS) vdW FTJ. Our calculations show that the FTJ we constructed can be switched from ferroelectric to antiferroelectric by changing the relative dipole arrangement of the BIS to form multiple nonvolatile resistance states. Since the charge transfer between the layers varies for the four different polarization states, the TER ratios range from 10³% to 10¹⁰%. The giant tunneling electroresistance and multiple resistance states in the 2D BIS-based FTJ suggest that it has great potential for application in nanoscale nonvolatile ferroelectric memory devices.

Proton-mediated reversible switching of metastable ferroelectric phases with low operation voltages

The exploration of ferroelectric phase transitions enables an in-depth understanding of ferroelectric switching and promising applications in information storage. However, controllably tuning the dynamics of ferroelectric phase transitions remains challenging owing to inaccessible hidden phases. Here, using protonic gating technology, we create a series of metastable ferroelectric phases and demonstrate their reversible transitions in layered ferroelectric α-In₂Se₃ transistors. By varying the gate bias, protons can be incrementally injected or extracted, achieving controllable tuning of the ferroelectric α-In₂Se₃ protonic dynamics across the channel and obtaining numerous intermediate phases. We unexpectedly discover that the gate tuning of α-In₂Se₃ protonation is volatile and the created phases remain polar. Their origin, revealed by first-principles calculations, is related to the formation of metastable hydrogen-stabilized α-In₂Se₃ phases. Furthermore, our approach enables ultralow gate voltage switching of different phases (below 0.4 volts). This work provides a possible avenue for accessing hidden phases in ferroelectric switching.

Reconfigurable Artificial Synapse Based on Ambipolar Floating Gate Memory

Artificial synapse networks capable of massively parallel computing and mimicking biological neural networks can potentially improve the processing efficiency of existing information technologies. Semiconductor devices functioning as excitatory and inhibitory synapses are crucial for developing intelligence systems, such as traffic control systems. However, achieving reconfigurability between two working modes (inhibitory and excitatory) and bilingual synaptic behavior in a single transistor remains challenging. This study successfully mimics a bilingual synaptic response using an artificial synapse based on an ambipolar floating gate memory comprising tungsten selenide (WSe₂)/hexagonal boron nitride (h-BN)/ molybdenum telluride (MoTe₂). In this WSe₂/h-BN/MoTe₂ structure, ambipolar semiconductors WSe₂ and MoTe₂ are inserted as channel and floating gates, respectively, and h-BN serves as the tunneling barrier layer. Using either positive or negative pulse amplitude modulations at the control gate, this device with bipolar channel conduction produced eight distinct resistance states. Based on this, we experimentally projected that we could achieve 490 memory states (210 hole-resistance states + 280 electron-resistance states). Using the bipolar charge transport and multistorage states of WSe₂/h-BN/MoTe₂ floating gate memory, we mimicked reconfigurable excitatory and inhibitory synaptic plasticity in a single device. Furthermore, the convolution neural network formed by these synaptic devices can recognize handwritten digits with an accuracy of >92%. This study identifies the unique properties of heterostructure devices based on two-dimensional materials as well as predicts their applicability in advanced recognition of neuromorphic computing.

Two-dimensional MoSi2As4-based field-effect transistors integrating switching and gas-sensing functions

Multifunctional nanoscale devices integrating multiple functions are of great importance for meeting the requirements of next-generation electronics. Herein, using first-principles calculations, we propose multifunctional devices based on the two-dimensional monolayer MoSi₂As₄, where a single-gate field-effect transistor (FET) and FET-type gas sensor are integrated. After introducing the optimizing strategies, such as underlap structures and dielectrics with a high dielectric constant (κ), we designed a 5 nm gate-length MoSi₂As₄ FET, whose performance fulfilled the key criteria of the International Technology Roadmap for Semiconductors (ITRS) for high-performance semiconductors. Under the joint adjustment of the underlap structure and high-κ dielectric material, the on/off ratio of the 5 nm gate-length FET reached up to 1.38 × 10⁴. In addition, driven by the high-performance FET, the MoSi₂As₄-based FET-type gas sensor showed a sensitivity of 38% for NH₃ and 46% for NO_2. Moreover, the weak interaction between NH₃ (NO₂) and MoSi₂As₄ favored the recycling of the sensor. Furthermore, the sensitivity of the sensor could be effectively improved by the gate voltage, and was increased up to 67% (74%) for NH₃ (NO₂). Our work provides theoretical guidance for the fabrication of multifunctional devices combining a high-performance FET and sensitive gas sensor.

Wurtzite and fluorite ferroelectric materials for electronic memory

Ferroelectric materials, the charge equivalent of magnets, have been the subject of continued research interest since their discovery more than 100 years ago. The spontaneous electric polarization in these crystals, which is non-volatile and programmable, is appealing for a range of information technologies. However, while magnets have found their way into various types of modern information technology hardware, applications of ferroelectric materials that use their ferroelectric properties are still limited. Recent advances in ferroelectric materials with wurtzite and fluorite structure have renewed enthusiasm and offered new opportunities for their deployment in commercial-scale devices in microelectronics hardware. This Review focuses on the most recent and emerging wurtzite-structured ferroelectric materials and emphasizes their applications in memory and storage-based microelectronic hardware. Relevant comparisons with existing fluorite-structured ferroelectric materials are made and a detailed outlook on ferroelectric materials and devices applications is provided.

Recent progress in the theoretical design of two-dimensional ferroelectric materials

Two-dimensional (2D) ferroelectrics (FEs), which maintain stable electric polarization in ultrathin films, are a promising class of materials for the development of various miniature functional devices. In recent years, several 2D FEs with unique properties have been successfully fabricated through experiments. They have been found to exhibit some unique properties either by themselves or when they are coupled with other functional materials (e.g., ferromagnetic materials, materials with 5d electrons, etc.). As a result, several new types of 2D FE functional devices have been developed, exhibiting excellent performance. As a type of newly discovered 2D functional material, the number of 2D FEs and the exploration of their properties are still limited and this calls for further theoretical predictions. This review summarizes recent progress in the theoretical predictions of 2D FE materials and provides strategies for the rational design of 2D FE materials. The aim of this review is to provide guidelines for the design of 2D FE materials and related functional devices.

Reconfigurable Physical Reservoir in GaN/α-In2Se3 HEMTs Enabled by Out-of-Plane Local Polarization of Ferroelectric 2D Layer

Significant effort for demonstrating a gallium nitride (GaN)-based ferroelectric metal-oxide-semiconductor (MOS)-high-electron-mobility transistor (HEMT) for reconfigurable operation via simple pulse operation has been hindered by the lack of suitable materials, gate structures, and intrinsic depolarization effects. In this study, we have demonstrated artificial synapses using a GaN-based MOS-HEMT integrated with an α-In₂Se₃ ferroelectric semiconductor. The van der Waals heterostructure of GaN/α-In₂Se₃ provides a potential to achieve high-frequency operation driven by a ferroelectrically coupled two-dimensional electron gas (2DEG). Moreover, the semiconducting α-In₂Se₃ features a steep subthreshold slope with a high ON/OFF ratio (∼10¹⁰). The self-aligned α-In₂Se₃ layer with the gate electrode suppresses the in-plane polarization while promoting the out-of-plane (OOP) polarization of α-In₂Se₃, resulting in a steep subthreshold slope (10 mV/dec) and creating a large hysteresis (2 V). Furthermore, based on the short-term plasticity (STP) characteristics of the fabricated ferroelectric HEMT, we demonstrated reservoir computing (RC) for image classification. We believe that the ferroelectric GaN/α-In₂Se₃ HEMT can provide a viable pathway toward ultrafast neuromorphic computing.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10³) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

General Theory for Bilayer Stacking Ferroelectricity

Two-dimensional (2D) ferroelectrics, which are rare in nature, enable high-density nonvolatile memory with low energy consumption. Here, we propose a theory of bilayer stacking ferroelectricity (BSF), in which two stacked layers of the same 2D material, with different rotation and translation, exhibit ferroelectricity. By performing systematic group theory analysis, we find all the possible BSF in all 80 layer groups (LGs) and discover the rules about the creation and annihilation of symmetries in the bilayer. Our general theory can not only explain all the previous findings (including sliding ferroelectricity), but also provide a new perspective. Interestingly, the direction of the electric polarization of the bilayer could be totally different from that of the single layer. In particular, the bilayer could become ferroelectric after properly stacking two centrosymmetric nonpolar monolayers. By means of first-principles simulations, we predict that the ferroelectricity and thus multiferroicity can be introduced to the prototypical 2D ferromagnetic centrosymmetric material CrI_{3} by stacking. Furthermore, we find that the out-of-plane electric polarization in bilayer CrI_{3} is interlocked with the in-plane electric polarization, suggesting that the out-of-plane polarization can be manipulated in a deterministic way through the application of an in-plane electric field. The present BSF theory lays a solid foundation for designing a large number of bilayer ferroelectrics and thus colorful platforms for fundamental studies and 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.

Electrically Switchable Polarization in Bi2 O2 Se Ferroelectric Semiconductors

Atomically 2D layered ferroelectric semiconductors, in which the polarization switching process occurs within the channel material itself, offer a new material platform that can drive electronic components toward structural simplification and high-density integration. Here, a room-temperature 2D layered ferroelectric semiconductor, bismuth oxychalcogenides (Bi₂ O₂ Se), is investigated with a thickness down to 7.3 nm (≈12 layers) and piezoelectric coefficient (d₃₃ ) of 4.4 ± 0.1 pm V^-1 . The random orientations and electrically dependent polarization of the dipoles in Bi₂ O₂ Se are separately uncovered owing to the structural symmetry-breaking at room temperature. Specifically, the interplay between ferroelectricity and semiconducting characteristics of Bi₂ O₂ Se is explored on device-level operation, revealing the hysteresis behavior and memory window (MW) formation. Leveraging the ferroelectric polarization originating from Bi₂ O₂ Se, the fabricated device exhibits "smart" photoresponse tunability and excellent electronic characteristics, e.g., a high on/off current ratio > 10⁴ and a large MW to the sweeping range of 47% at V_GS  = ±5 V. These results demonstrate the synergistic combination of ferroelectricity with semiconducting characteristics in Bi₂ O₂ Se, laying the foundation for integrating sensing, logic, and memory functions into a single material system that can overcome the bottlenecks in von Neumann architecture.

Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.

Ultrafast carrier dynamics and layer-dependent carrier recombination rate in InSe

InSe layered semiconductors with high mobility have advantages over transition-metal dichalcogenides in certain device applications. Understanding the dynamics of carriers, especially around the major bandgaps, is not only of fundamental interest but also important for improving the performance of devices. We investigated ultrafast carrier dynamics in exfoliated InSe near the bandgap and found that the presence of photocarriers led to shrinkage in the optical bandgap. In addition, we observed that the carrier recombination rate increased when the thickness of the InSe nanoflakes was reduced and the process was dominated by surface recombination. For the same flakes, the recombination rate became lower after the freshly exfoliated InSe was exposed to air and oxidized. Using a free carrier diffusion model, layer-dependent surface recombination velocities were obtained. Our investigation reveals that the surface condition and the thickness of few-layer InSe play important roles in carrier lifetimes.