A 2D-SnSe film with ferroelectricity and its bio-realistic synapse application

Identification and Manipulation of Atomic Defects in Monolayer SnSe

SnSe, an environmental-friendly group-IV monochalcogenide semiconductor, demonstrates outstanding performance in various applications ranging from thermoelectric devices to solar energy harvesting. Its ultrathin films show promise in the fabrication of ferroelectric nonvolatile devices. However, the microscopic identification and manipulation of point defects in ultrathin SnSe single crystalline films, which significantly impact their electronic structure, have been inadequately studied. This study presents a comprehensive investigation of point defects in monolayer SnSe films grown via molecular beam epitaxy. By combining scanning tunneling microscopy (STM) characterization with first-principles calculations, we identified four types of atomic/molecular vacancies, four types of atomic substitutions, and three types of extrinsic defects. Notably, we have demonstrated the ability to convert a substitutional defect into a vacancy and to reposition an adsorbate by manipulating a single atom or molecule using an STM tip. We have also analyzed the local atomic displacement induced by the vacancies. This work provides a solid foundation for engineering the electronic structure of future SnSe-based nanodevices.

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.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Van der Waals Layer Transfer of 2D Materials for Monolithic 3D Electronic System Integration: Review and Outlook

Two-dimensional materials (2DMs) have attracted a great deal of interest due to their immense potential for scientific breakthroughs and technological innovations. While some 2D transition metal dichalcogenides (TMDC) such as MoS₂ and WS₂ are considered as the ultimate channel materials in unltrascaled transistors as replacements for Si, there has also been increasing interest in the monolithic 3D integration of 2DMs on the Si CMOS platform or in flexible electronics as back-end-of-line transistors, memory devices/selectors, and sensors, taking advantage of 2DM properties such as a high current driving capability with low leakage current, nonvolatile switching characteristics, a large surface-to-volume ratio, and a tunable bandgap. However, the realization of both of these scenarios critically depends on the development of manufacturing-viable high-yield 2DM layers transfer from the growth substrate to the Si, since the growth of high-quality 2DM layers often requires a high-temperature growth process on template substrates. Motivated by this, extensive efforts have been made by the 2DM research community to develop various 2DM layer transfer methods, leveraging the van der Waals transfer capability of the layer-structured 2DMs. These efforts have led to a number of successful demonstrations of wafer-scale 2D TMDC layer transfer, while 2DM-enabled template growth/transfer of some functional bulk materials such as III-V, Ge, and AlN has also been demonstrated. This review surveys and compares different 2DM transfer methods developed recently, with a focus on large-area 2D TMDC film transfer along with an introduction of 2DM template-assisted van der Waals growth/transfer of non-2D thin films. We will also briefly present an outlook of our envisioned multifunctionalities in 3D integrated electronic systems enabled by monolithic 3D integration of 2DMs and III-V via van der Waals transfer and discuss possible technology options for overcoming remaining challenges.

Ferroelectric Field-Effect-Transistor Integrated with Ferroelectrics Heterostructure

To address the demands of emerging data-centric computing applications, ferroelectric field-effect transistors (Fe-FETs) are considered the forefront of semiconductor electronics owing to their energy and area efficiency and merged logic-memory functionalities. Herein, the fabrication and application of an Fe-FET, which is integrated with a van der Waals ferroelectrics heterostructure (CuInP₂ S₆ /α-In₂ Se₃ ), is reported. Leveraging enhanced polarization originating from the dipole coupling of CIPS and α-In₂ Se₃ , the fabricated Fe-FET exhibits a large memory window of 14.5 V at V_GS = ±10 V, reaching a memory window to sweep range of ≈72%. Piezoelectric force microscopy measurements confirm the enhanced polarization-induced wider hysteresis loop of the double-stacked ferroelectrics compared to single ferroelectric layers. The Landau-Khalatnikov theory is extended to analyze the ferroelectric characteristics of a ferroelectric heterostructure, providing detailed explanations of the hysteresis behaviors and enhanced memory window formation. The fabricated Fe-FET shows nonvolatile memory characteristics, with a high on/off current ratio of over 10⁶ , long retention time (>10⁴ s), and stable cyclic endurance (>10⁴ cycles). Furthermore, the applicability of the ferroelectrics heterostructure is investigated for artificial synapses and for hardware neural networks through training and inference simulation. These results provide a promising pathway for exploring low-dimensional ferroelectronics.

First observation on emergence of strong room-temperature ferroelectricity and multiferroicity in 2D-Ti3C2Tx free-standing MXene film

Two-dimensional (2D) multiferroics are key candidate materials towards advancement of smart technology. Here, we employed a simple synthesis approach to address the long-awaited dream of developing ferroelectric and multiferroic 2D materials, especially in the new class of materials called MXenes. The etched Ti₃C₂T_x MXene was first synthesized after HF-treatment followed by a delamination process for successful synthesis of free-standing Ti₃C₂T_x film. The free-standing film was then exposed to air at room-temperature and heated at different temperatures to form a TiO₂ layer derived from the Ti₃C₂T_x MXene itself. The ferroelectric measurement showed a clear polarization hysteresis loop at room-temperature. Also, due to the reported ferromagnetic behavior of Ti₃C₂T_x MXene, our composite could show multiferroic properties at room-temperature. The magnetoelectric coupling test was also performed that showed a clear, switchable spontaneous polarization under applied magnetic field. TiO₂ is reported to be an incipient ferroelectric that assumes a ferroelectric phase in composite form. The structural and morphological analysis confirmed successful synthesis of free-standing film and the Raman spectroscopy revealed the formation of different phases of TiO₂ and the observed ferroelectricity could be due to structural deformation as a result of the formation of this new phase. The measured value of remanent polarization is 0.5 μC cm⁻². This is the first report on the existence of a ferroelectric phase and multiferroic coupling in 2D free-standing MXene film at room-temperature which opens-up the possibility of 2D material-based electric and magnetic data storage applications at room-temperature.

Two-dimensional (2D) multiferroics are key candidate materials towards advancement of smart technology.

Bioresistive Random-Access Memory with Gold Nanoparticles that Generate the Coulomb Blocking Effect Can Realize Multilevel Data Storage and Synapse Simulation

Gold nanoparticles (Au NPs) have good biocompatibility and special quantum effects. In this Letter, we embedded Au NPs into silkworm hemolymph (SH) to improve the performance of the device and fabricated Al/SH:Au NPs/indium tin oxide (ITO)/glass resistive random access memory. The device exhibits a bipolar switching behavior with a retention time of 10⁴ s. Compared with the Al/SH/ITO device without Au NPs, the device has a higher ON/OFF current ratio (>10⁵) and a smaller V_reset. The improvement in device performance is attributed to the fact that Au NPs act as the electron-trapping center in the device; a Coulomb blockade occurs after electrons are trapped, thereby increasing the resistance of the device in the high-resistance state. Using optimized devices can realize multilevel data storage and can also simulate synaptic characteristics such as potentiation and depression. The device is expected to be applied to high-density, low-cost, degradable, and biocompatible storage devices and neuromorphic computing in the future.

Artificial Neuron and Synapse Devices Based on 2D Materials

Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next-generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material-based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling-bond-free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials-based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all-or-nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials-based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials-based neuromorphic devices are discussed.