Quantum Conductance in Vertical Hexagonal Boron Nitride Memristors with Graphene-Edge Contacts.

Two-dimensional materials (2DMs) have gained significant interest for resistive-switching memory toward neuromorphic and in-memory computing (IMC). To achieve atomic-level miniaturization, we introduce vertical hexagonal boron nitride (h-BN) memristors with graphene edge contacts. In addition to enabling three-dimensional (3D) integration (i.e., vertical stacking) for ultimate scalability, the proposed structure delivers ultralow power by isolating single conductive nanofilaments (CNFs) in ultrasmall active areas with negligible leakage thanks to atomically thin (∼0.3 nm) graphene edge contacts. Moreover, it facilitates studying fundamental resistive-switching behavior of single CNFs in CVD-grown 2DMs that was previously unattainable with planar devices. This way, we studied their programming characteristics and observed a consistent single quantum step in conductance attributed to unique atomically constrained nanofilament behavior in CVD-grown 2DMs. This resistive-switching property was previously suggested for h-BN memristors and linked to potential improvements in stability (robustness of CNFs), and now we show experimental evidence including superior retention of quantized conductance.

2D Dual Gate Field-Effect Transistor Enabled Versatile Functions.

Advanced computing technologies such as distributed computing and the Internet of Things require highly integrated and multifunctional electronic devices. Beyond the Si technology, 2D-materials-based dual-gate transistors are expected to meet these demands due to the ultra-thin body and the dangling-bond-free surface. In this work, a molybdenum disulfide (MoS2 ) asymmetric-dual-gate field-effect transistor (ADGFET) with an In2 Se3 top gate and a global bottom gate is designed. The independently controlled double gates enable the device to achieve an on/off ratio of 106 with a low subthreshold swing of 94.3 mV dec-1 while presenting a logic function. The coupling effect between the double gates allows the top gate to work as a charge-trapping layer, realizing nonvolatile memory (105 on/off ratio with retention time over 104 s) and six-level memory states. Additionally, ADGFET displays a tunable photodetection with the responsivity reaching the highest value of 857 A W-1 , benefiting from the interface coupling between the double gates. Meanwhile, the photo-memory property of ADGFET is also verified by using the varying exposure dosages-dependent illumination. The multifunctional applications demonstrate that the ADGFET provides an alternative way to integrate logic, memory, and sensing into one device architecture.

The Nonvolatile Memory and Neuromorphic Simulation of ReS2/h-BN/Graphene Floating Gate Devices Under Photoelectrical Hybrid Modulations.

The floating gate devices, as a kind of nonvolatile memory, obtain great application potential in logic-in-memory chips. The 2D materials have been greatly studied due to atomically flat surfaces, higher carrier mobility, and excellent photoelectrical response. The 2D ReS2 flake is an excellent candidate for channel materials due to thickness-independent direct bandgap and outstanding optoelectronic response. In this paper, the floating gate devices are prepared with the ReS2/h-BN/Gr heterojunction. It obtains superior nonvolatile electrical memory characteristics, including a higher memory window ratio (81.82%), tiny writing/erasing voltage (±8 V/2 ms), long retention (>1000 s), and stable endurance (>1000 times) as well as multiple memory states. Meanwhile, electrical writing and optical erasing are achieved by applying electrical and optical pulses, and multilevel storage can easily be achieved by regulating light pulse parameters. Finally, due to the ideal long-time potentiation/depression synaptic weights regulated by light pulses and electrical pulses, the convolutional neural network (CNN) constructed by ReS2/h-BN/Gr floating gate devices can achieve image recognition with an accuracy of up to 98.15% for MNIST dataset and 91.24% for Fashion-MNIST dataset. The research work adds a powerful option for 2D materials floating gate devices to apply to logic-in-memory chips and neuromorphic computing.

Delay characteristics of quasi-nonvolatile memory devices operating in positive feedback mechanism.

This study examines the memory and read delay characteristics of quasi-nonvolatile memory (QNVM) devices operating in a positive feedback mechanism through technology computer-aided design simulation. The QNVM devices exhibit a rapid operation speed of 5 ns, a significant sensing margin of approximately 8.0µA, and a retention time of around 1 s without any external bias. These devices showcase an exceptionally brief read delay of 0.12 ns. The energy band diagrams during the memory operation are analyzed to clarify the factors influencing the read delay. The write and standby conditions modulate the potential barrier height during the standby operation, thereby affecting the read delay. Moreover, the shorter rising time causes the reduction of the read delay. This study demonstrates that the QNVM device has the potential to resolve energy consumption and speed issues in nonvolatile memory devices.

Indium-Gallium-Zinc Oxide-Based Synaptic Charge Trap Flash for Spiking Neural Network-Restricted Boltzmann Machine.

Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.

Black Phosphorus/Ferroelectric P(VDF-TrFE) Field-Effect Transistors with High Mobility for Energy-Efficient Artificial Synapse in High-Accuracy Neuromorphic Computing.

The neuromorphic system is an attractive platform for next-generation computing with low power and fast speed to emulate knowledge-based learning. Here, we design ferroelectric-tuned synaptic transistors by integrating 2D black phosphorus (BP) with a flexible ferroelectric copolymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)). Through nonvolatile ferroelectric polarization, the P(VDF-TrFE)/BP synaptic transistors show a high mobility value of 900 cm2 V-1 s-1 with a 103 on/off current ratio and can operate with low energy consumption down to the femtojoule level (∼40 fJ). Reliable and programmable synaptic behaviors have been demonstrated, including paired-pulse facilitation, long-term depression, and potentiation. The biological memory consolidation process is emulated through ferroelectric gate-sensitive neuromorphic behaviors. Inspiringly, the artificial neural network is simulated for handwritten digit recognition, achieving a high recognition accuracy of 93.6%. These findings highlight the prospects of 2D ferroelectric field-effect transistors as ideal building blocks for high-performance neuromorphic networks.

Sub-Nanosecond Switching of Si:HfO2 Ferroelectric Field-Effect Transistor.

The discovery of ferroelectric doped HfO2 enabled the emergence of scalable and CMOS-compatible ferroelectric field-effect transistor (FeFET) technology which has the potential to meet the growing need for fast, low-power, low-cost, and high-density nonvolatile memory, and neuromorphic devices. Although HfO2 FeFETs have been widely studied in the past few years, their fundamental switching speed is yet to be explored. Importantly, the shortest polarization time demonstrated to date in HfO2-based FeFET was ∼10 ns. Here, we report that a single subnanosecond pulse can fully switch HfO2-based FeFET. We also study the polarization switching kinetics across 11 orders of magnitude in time (300 ps to 8 s) and find a remarkably steep time-voltage relation, which is captured by the classical nucleation theory across this wide range of pulse widths. These results demonstrate the high-speed capabilities of FeFETs and help better understand their fundamental polarization switching speed limits and switching kinetics.

High-Speed Optoelectronic Nonvolatile Memory Based on van der Waals Heterostructures.

High-performance optoelectronic nonvolatile memory is promising candidate for next-generation information memory devices. Here, a floating-gate memory is constructed based on van der Waals heterostructure, which exhibits a large storage window ratio (≈75.5%) and an extremely high on/off ratio (107 ), as well as an ultrafast electrical writing/erasing speed (40 ns). The enhanced performance enables as-fabricated devices to present excellent multilevel data storage, robust retention, and endurance performance. Moreover, stable optical erasing operations can be achieved by illuminating the device with a laser pulse, showcasing outstanding optoelectronic storage performance (optical erasing speed ≈ 2.3 ms). The nonvolatile and high-speed characteristics of these devices hold significant potential for the integration of high-performance nonvolatile memory.

Multifunctional Half-Floating-Gate Field-Effect Transistor Based on MoS2-BN-Graphene van der Waals Heterostructures.

Multifunctional electronic devices that combine logic operation and data storage functions are of great importance in developing next-generation computation. The recent development of van der Waals (vdW) heterostructures based on various two-dimensional (2D) materials have brought exceptional opportunities in designing novel electronic devices. Although various 2D-heterostructure-based electronic devices have been reported, multifunctional devices that can combine logic operations and data storage functions are still quite rare. In this work, we design and fabricate a half-floating-gate field-effect transistor based on MoS2-BN-graphene vdW heterostuctures, which can be used for logic operations as a MOSFET, nonvolatile memory as a floating-gate MOSFET (FG-MOSFET), and rectification as a diode. These results could lay the foundation for various applications based on 2D vdW heterostuctures and inspire the design of next-generation computation beyond the von Neumann architecture.

Study of carbon nanotube embedded honey as a resistive switching material.

In this paper, natural organic honey embedded with carbon nanotubes (CNTs) was studied as a resistive switching material for biodegradable nonvolatile memory in emerging neuromorphic systems. CNTs were dispersed in a honey-water solution with the concentration of 0.2 wt% CNT and 30 wt% honey. The final honey-CNT-water mixture was spin-coated and dried into a thin film sandwiched in between Cu bottom electrode and Al top electrode to form a honey-CNT based resistive switching memory (RSM). Surface morphology, electrical characteristics and current conduction mechanism were investigated. The results show that although CNTs formed agglomerations in the dried honey-CNT film, both switching speed and the stability in SET and RESET process of honey-CNT RSM were improved. The mechanism of current conduction in CNT is governed by Ohm's law in low-resistance state and the low-voltage range in high-resistance state, but transits to the space charge limited conduction at high voltages approaching the SET voltage.

Freestandingα-rhombohedral borophene nanosheets: preparation and memory device application.

Borophene has attracted extensive interests owing to its distinct structural, electronic and optical properties for promising potential applications. However, the structural instability and need of metal substrate for deposition of borophene seriously restrict the exploration of its exceptional physical and chemical properties and further hamper its extensive applications towards high-performance electronic and optoelectronic devices. Here, we reported the synthesis of high-quality freestandingα-rhombohedral borophene nanosheets by a facile probe ultrasonic approach in different organic solvents. The results show that the nanosheets have high-quality in ethanol solution and have an average lateral size of 0.54µm and a thickness of around 1.2 nm. Photoluminescence spectra indicate that a strong quantum confinement effect occurs in the nanosheets, which caused the increase of the band gap from 1.80 eV for boron powders and 2.52 eV for the nanosheets s. A nonvolatile memory device based on the nanosheets mixed with polyvinylpyrrolidone was fabricated, which exhibited a good rewriteable nonvolatile memory behavior and good stability.

Bipolar Resistive Switching in Junctions of Gallium Oxide and p-type Silicon.

In this work, native GaOx is positioned between bulk gallium and degenerately doped p-type silicon (p+-Si) to form Ga/GaOx/SiOx/p+-Si junctions. These junctions show memristive behavior, exhibiting large current-voltage hysteresis. When cycled between -2.5 and 2.5 V, an abrupt insulator-metal transition is observed that is reversible when the polarity is reversed. The ON/OFF ratio between the high and low resistive states in these junctions can reach values on the order of 108 and retain the ON and OFF resistive states for up to 105 s with an endurance exceeding 100 cycles. The presence of a nanoscale layer of gallium oxide is critical to achieving reversible resistive switching by formation and dissolution of the gallium filament across the switching layer.

Trap-Assisted Charge Storage in Titania Nanocrystals toward Optoelectronic Nonvolatile Memory.

Transistor-based memories are of particular significance in the pursuit of next-generation nonvolatile memories. The charge storage medium in a transistor-based memory is pivotal to the device performance. In this report, nitrogen doping titania nanocrystals (N-TiO2 NCs) synthesized through a low-temperature nonhydrolytic method are used as the charge storage medium in a graphene transistor-based memory. The decoration of the N-TiO2 NCs enables the device to perform as an ultraviolet (UV) light-programmable nonvolatile optoelectronic memory. Multilevel nonvolatile information recording can be realized through accurate control of the incident light dose, which is ascribed to the vast and firm hole trapping abilities of the N-TiO2 NCs induced by the N dopant. Accordingly, a positive gate voltage can be used to erase the programmed state by promoting the recombination of stored holes in N-TiO2 NCs. This study manifests the importance of trap engineering for information storage and provides an alternative path toward nonvolatile optoelectronic memory.

Dielectric Engineered Two-Dimensional Neuromorphic Transistors.

Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable synaptic arrays with complementary metal-oxide-semiconductor compatibility remains challenging. Here, we report a 2D transition metal dichalcogenide-based synaptic array fabricated on commercial silicon-rich silicon nitride (sr-SiNx) substrate. The array demonstrates uniform performance with sufficiently high analogue on/off ratio and linear conductance update, and low cycle-to-cycle variability (1.5%) and device-to-device variability (5.3%), which are essential for neuromorphic hardware implementation. On the basis of the experimental data, we further prove that the artificial synapses can achieve a recognition accuracy of 91% on the MNIST handwritten data set. Our findings offer a simple approach to achieve 2D synaptic arrays by using an industry-compatible sr-SiNx dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.

Toward Highly Robust Nonvolatile Multilevel Memory by Fine Tuning of the Nanostructural Crystalline Solid-State Order.

Organic resistive memory (ORM) offers great promise for next-generation high-density multilevel-cell (MLC) data storage. However, the fine tuning of crystalline order among its active layer still remains challenging, which largely restricts ORM behavior. Here, an exceptional solid-state transition from disordered orientations to highly-uniform orientation within the ORM layer is facilely triggered via molecular strategic tailoring. Two diketopyrrolopyrrole-based small molecular analogues (NI1 TDPP and NI2 TDPP) are demonstrated to display different symmetry. The asymmetric NI1 TDPP shows an irregular solid-state texture, while the centro-symmetric NI2 TDPP conforms to an ordered out-of-plane single-crystalline pattern that aligns with the foremost charge transportation along the substrate normal, and exhibits excellent MLC memory characteristics. Moreover, this highly oriented pattern guarantees the large-area film uniformity, leading to the twofold increase in the yield of as-fabricated ORM devices. This study reveals that the solid-state crystalline nanostructural order of organic materials can be controlled by reasonable molecular design to actuate high-performance organic electronic circuits.

Digital and analog functionality in monolayer AlOx-based memristors with various oxidizer sources.

Memristors with the outstanding advantages are beneficial for neuromorphic computing and next-generation storage. Realizing various resistive switching behaviors in monolayer memristors is essential for understanding the device physics and fabricating fully memristive devices. In this paper, a simple and feasible method was proposed to achieve the digital and analog resistive switching in Cu/AlOx/Ag memristors by using ozone and water precursors in atomic layer deposition. According to the characterization results of surface topography, Raman spectrum and electrical measurement, the transition between the abrupt and gradual resistive switching was ascribed to the migration and diffusion of active electrode metal ions in the sparser, rougher and more amorphous AlOx dielectric films. The key features of biological synapses including long-term potentiation/depression, paired-pulse facilitation and learning-experience behaviors were emulated in the analog monolayer memristors. This study makes an important step towards the development of the sophisticated, multi-functional, and large-scale integrated neuromorphic devices and systems.

Ferroelectric field-effect transistors based on HfO2: a review.

In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs) based on ferroelectric hafnium oxide (HfO2), ten years after the first report on such a device. With a focus on the use of FeFET for nonvolatile memory application, we discuss its basic operation principles, switching mechanisms, device types, material properties and array structures. Key device performance metrics such as cycling endurance, retention, memory window, multi-level operation and scaling capability are analyzed. We also briefly survey recent developments in alternative applications for FeFETs including neuromorphic and in-memory computing as well as radiofrequency devices.

Magnetic Domain Wall Based Synaptic and Activation Function Generator for Neuromorphic Accelerators.

Magnetic domain walls are information tokens in both logic and memory devices and hold particular interest in applications such as neuromorphic accelerators that combine logic in memory. Here, we show that devices based on the electrical manipulation of magnetic domain walls are capable of implementing linear, as well as programmable nonlinear, functions. Unlike other approaches, domain-wall-based devices are ideal for application to both synaptic weight generators and thresholding in deep neural networks. Prototype micrometer-size devices operate with 8 ns current pulses and the energy consumption required for weight modulation is ≤16 pJ. Both speed and energy consumption compare favorably to other synaptic nonvolatile devices, with the expected energy dissipation for scaled 20 nm devices close to that of biological neurons.

Molecularly Thin Electrolyte for All Solid-State Nonvolatile Two-Dimensional Crystal Memory.

A molecularly thin electrolyte is developed to demonstrate a nonvolatile, solid-state, one-transistor (1T) memory based on an electric-double-layer (EDL) gated WSe2 field-effect transistor (FET). The custom-designed monolayer electrolyte consists of cobalt crown ether phthalocyanine and lithium ions, which are positioned by field-effect at either the surface of the WSe2 channel or an h-BN capping layer to achieve "1" or "0", respectively. Bistability in the monolayer electrolyte memory is significantly improved by the h-BN cap with density functional theory (DFT) calculations showing enhanced trapping of Li+ near h-BN due to a ∼1.34 eV increase in the absolute value of the adsorption energy compared to vacuum. The threshold voltage shift between the two states corresponds to a change in charge density of ∼2.5 × 1012 cm-2, and an On/Off ratio exceeding 104 at a back gate voltage of 0 V. The On/Off ratio remains stable after 1000 cycles and the retention time for each state exceeds 6 h (max measured). When the write time approaches 1 ms, the On/Off ratio remains >102, showing that the monolayer electrolyte-gated FET can respond on time scales similar to existing flash memory. The data suggest that faster switching times and lower switching voltages could be feasible by top gating.

A Tunneling Dielectric Layer Free Floating Gate Nonvolatile Memory Employing Type-I Core-Shell Quantum Dots as Discrete Charge-Trapping/Tunneling Centers.

A nonvolatile memory with a floating gate structure is fabricated using ZnSe@ZnS core-shell quantum dots as discrete charge-trapping/tunneling centers. Systematical investigation reveals that the spontaneous recovery of the trapped charges in the ZnSe core can be effectively avoided by the type-I energy band structure of the quantum dots. The surface oleic acid ligand surrounding the quantum dots can also play a role of energy barrier to prevent unintentional charge recovery. The device based on the quantum dots demonstrates a large memory window, stable retention, and good endurance. What is more, integrating charge-trapping and tunneling components into one quantum dot, which is solution synthesizable and processible, can largely simplify the processing of the floating gate nonvolatile memory. This research reveals the promising application potential of type-I core-shell nanoparticles as the discrete charge-trapping/tunneling centers in nonvolatile memory in terms of performance, cost, and flexibility.