Multilayer Hexagonal Boron Nitride Research Articles

Integration of Ag-based threshold switching devices in silicon microchips

Threshold-type resistive switching (RS) is an essential electronic behavior in many types of integrated circuits and can be exploited in multiple applications, such as leaky integrate-and-fire neurons for artificial neural networks (ANNs). Many research articles have shown that metal/insulator/metal (MIM) devices using Ag electrodes exhibit stable threshold-type RS, but all of them presented large devices (>1 µm2) fabricated on unfunctional SiO2/Si substrates. In this article, for the first time we integrate Ag-based threshold-type RS devices at the back-end-of-line interconnections of silicon microchips. The insulator used is multilayer hexagonal boron nitride (h-BN), and the size of the devices is ∼0.05 µm2. The devices can switch between a high resistive state (HRS) and a low resistive state (LRS) without the need of any forming process, and we observe a high endurance over 1 million cycles over multiple devices. By performing circuit simulation using SPICE software, we confirm that this electrical behavior is suitable for being used as leaky integrate-and-fire electronic neuron in spiking neural networks for image recognition, and the h-BN artificial neurons operate correctly for 94 % of the images presented. Our study represents a significant advancement towards the integration of Ag-based threshold-type RS devices in silicon microchips.

Molecular dynamics simulation of the thermal conductivity mechanism of polydimethylsiloxane composites filled by multilayer hexagonal boron nitride

To design polydimethylsiloxane (PDMS)-based nanocomposites with high thermal conductivity, multiscale simulations were adopted to study the effect of the modified hexagonal boron nitride (h-BN) and the layer number of h-BN on the interface improvement of nanocomposites. The results indicate that 6-aminocaproic acid (ACA) effectively improves the interfacial thermal conductivity of the composites. The interaction energy between h-BN modified by ACA (ACA@h-BN) and PDMS is enhanced compared to that of an unmodified h-BN/PDMS composite system. It's interesting that the change rate of thermal conductivity (δ) decreases first and then increases with the increase in the layer number of ACA@h-BN, and gradually increases to a value more equal to that of the single-layer system when the layer number of ACA@h-BN reaches 5. The interfacial thickness increases from 1.93 Å to 2.60 Å as the layer number of ACA@h-BN increases from 1 to 6, which is beneficial to improve the thermal conductivity. However, the matching coefficient (M) of the phonon vibration power spectrum between PDMS and ACA@h-BN decreases from 0.024 to 0.011 as the layer number of ACA@h-BN increases from 1 to 6, which impairs the thermal conductivity. The decrease in M dominates the δ in the composites filled by h-BN with 1–3 layers. The thermal conductivity is more affected by the specific gravity of ACA@h-BN than M in the composites filled by h-BN with 4–6 layers. It is proposed that the ideal single-layer ACA@h-BN can be replaced to some extent by the ACA@h-BN with 5 layers to improve the thermal properties of the composites. This work is expected to provide theoretical support for interface modification of nanocomposites, and preparation of high-thermal conductivity composites.

Memristive circuits based on multilayer hexagonal boron nitride for millimetre-wave radiofrequency applications

Memristive circuits based on multilayer hexagonal boron nitride for millimetre-wave radiofrequency applications

Chiral organic molecular structures supported by planar surfaces.

We employ the molecular dynamics simulations to study the dynamics of acetanilide (ACN) molecules placed on a flat surface of planar multilayer hexagonal boron nitride. We demonstrate that the ACN molecules, known to be achiral in the three-dimensional space, become chiral after being placed on the substrate. Homochirality of the ACN molecules leads to stable secondary structures stabilized by hydrogen bonds between peptide groups of the molecules. By employing molecular dynamics simulations, we reveal that the structure of the resulting hydrogen-bond chains depends on the isomeric composition of the molecules. If all molecules are homochiral (i.e., with only one isomer being present), they form secondary structures (chains of hydrogen bonds in the shapes of arcs, circles, and spirals). If the molecules at the substrate form a racemic mixture, then no regular secondary structures appear, and only curvilinear chains of hydrogen bonds of random shapes emerge. A hydrogen-bond chain can form a zigzag array only if it has an alternation of isomers. Such chains can create two-dimensional (2D) regular lattices or 2D crystals. The melting scenarios of such 2D crystals depend on density of its coverage of the substrate. At 25% coverage, melting occurs continuously in the temperature interval 295-365K. For a complete coverage, melting occurs at 415-470 K due to a shift of 11% of all molecules into the second layer of the substrate.

Reliably straining suspended van der Waals heterostructures

2D materials provide a rapidly expanding platform for the observation of novel physical phenomena and for the realization of cutting-edge optoelectronic devices. In addition to their peculiar individual characteristics, 2D materials can be stacked into complex van der Waals heterostructures, greatly expanding their potential. Moreover, thanks to their excellent stretchability, strain can be used as a powerful control knob to tune or boost many of their properties. Here, we present a novel method to reliably and repeatedly apply a high uniaxial tensile strain to suspended van der Waals heterostructures. The reported device is engineered starting from a silicon-on-insulator substrate, allowing for the realization of suspended silicon beams that can amplify the applied strain. The strain module functionality is demonstrated using single- and double-layer graphene layers stacked with a multilayered hexagonal boron nitride flake. The heterostructures can be uniaxially strained, respectively, up to ∼1.2% and ∼1.8%.

Thermal stability, work function and Fermi level analysis of 2D multi-layered hexagonal boron nitride films

Thermal stability and Fermi level analysis of commercial multilayer (ML) and as-grown 10 nm thick hexagonal boron nitride (hBN) films on Cu foil by atmospheric chemical vapor deposition (APCVD) by heating in N2 and air at temperatures ≤900 °C are studied. For all samples, progressive surface oxidation was observed correlating with higher hBN nucleation density. Thermal stability was measured in air from 200 °C to 400 °C (relative humidity ∼65%) where commercial ML and APCVD material degraded; BN peaks begin to be replaced by BO peaks at temperatures >300 °C. It is shown that hBN thermal stability is sensitive to Tgrowth and Cu foil surface preparation methods such as electropolishing (EP). We evaluate as-grown material for different Tgrowth conditions (1000 °C, 1030 °C, 1050 °C and 1060 °C) by ultraviolet photoelectron spectroscopy (UPS) to locate and understand subsequent Fermi level and work function variation, confirming our material is in general n-type, which becomes increasingly so as thermal stability regresses. To this effect, we observe a work function increase of 0.45 eV as BO increases with in-situ anneal temperature. An average electron affinity of 2.13 eV and 1.7 eV is determined for commercial hBN and APCVD material, respectively. Further work is required by material scientists to optimize the material properties of CVD hBN.

Near- and Far-Field Observation of Phonon Polaritons in Wafer-Scale Multilayer Hexagonal Boron Nitride Prepared by Chemical Vapor Deposition.

Polaritons in layered materials (LMs) are a promising platform to manipulate and control light at the nanometer scale. Thus, the observation of polaritons in wafer-scale LMs is critically important for the development of industrially relevant nanophotonics and optoelectronics applications. In this work, phonon polaritons (PhPs) in wafer-scale multilayer hexagonal boron nitride (hBN) grown by chemical vapor deposition are reported. By infrared nanoimaging, the PhPs are visualized, and PhP lifetimes of ≈0.6ps are measured, comparable to that of micromechanically exfoliated multilayer hBN. Further, PhP nanoresonators are demonstrated. Their quality factors of ≈50 are about 0.7 times that of state-of-the-art devices based on exfoliated hBN. These results can enable PhP-based surface-enhanced infrared spectroscopy (e.g., for gas sensing) and infrared photodetector applications.

Thickness-Controlled Synthesis of Uniform Hexagonal Boron Nitride Films on Metal Alloys by Chemical Vapor Deposition

Two-dimensional (2D) multilayer hexagonal boron nitride (hBN) is a superior substrate for other 2D materials due to its insulating property, atomic-scale smoothness, and absence of dangling bonds, enhancing electrical or optical properties in high-performance 2D material-based devices. In most fabrication processes for 2D heterostructure devices, thick hBN flakes exfoliated from bulk have been used because of their high quality and simplicity in the process. However, exfoliated hBN flakes are limited in size and thickness control, impeding scale-up production for industrialization. Therefore, new synthesis strategies are required to overcome the drawback of the exfoliation method. In this work, we investigated the synthesis of large-area, multilayer hBN via atmospheric pressure chemical vapor deposition using single crystalline metal and single crystal alloy substrates. The synthesized results are characterized by optical microscopy, scanning electron microscopy, Raman spectroscopy, atomic force microscopy, etc. These studies provide a more comprehensive understanding of the growth mechanism of hexagonal boron nitride.

Conductance quantization in h-BN memristors

Memristive devices made of multilayer hexagonal boron nitride are attracting much attention for information storage, computation, encryption, and communication. Generating multilevel operation in these memristors would be an important added value for the field of neuromorphic computation, but the main phenomenon producing it (i.e., conductance quantization) is not well understood for this material. Here, we analyze hexagonal boron nitride memristive devices with metal electrodes operating in their quasistatic and transient regimes. Conductance quantization effects were observed both in I-V curves measured under ramped voltage stress and in the reset processes driven by voltage pulse trains. Polar and time lag plots are employed to shed light on the physics behind the device resistive switching. A model is proposed to reproduce the electrical characteristics of progressive reset processes and the discretization of the device conductance linked to quantum effects.

Resistive Switching and Current Conduction Mechanisms in Hexagonal Boron Nitride Threshold Memristors with Nickel Electrodes

AbstractThe 2D insulating material hexagonal boron nitride (h‐BN) has attracted much attention as the active medium in memristive devices due to its favorable physical properties, among others, a wide bandgap that enables a large switching window. Metal filament formation is frequently suggested for h‐BN devices as the resistive switching (RS) mechanism, usually supported by highly specialized methods like conductive atomic force microscopy (C‐AFM) or transmission electron microscopy (TEM). Here, the switching of multilayer hexagonal boron nitride (h‐BN) threshold memristors with two nickel (Ni) electrodes is investigated through their current conduction mechanisms. Both the high and the low resistance states are analyzed through temperature‐dependent current–voltage measurements. The formation and retraction of nickel filaments along boron defects in the h‐BN film as the resistive switching mechanism is proposed. The electrical data are corroborated with TEM analyses to establish temperature‐dependent current–voltage measurements as a valuable tool for the analysis of resistive switching phenomena in memristors made of 2D materials. The memristors exhibit a wide and tunable current operation range and low stand‐by currents, in line with the state of the art in h‐BN‐based threshold switches, a low cycle‐to‐cycle variability of 5%, and a large On/Off ratio of 107.

Modeling the Variability of Au/Ti/h-BN/Au Memristive Devices

The variability of memristive devices using multilayer hexagonal boron nitride (h-BN) coupled with Ti and Au electrodes (i.e., Au/Ti/h-BN/Au) is analyzed in depth using different numerical techniques. We extract the reset voltage using three different methods, quantify its cycle-to-cycle variability, calculate the charge and flux that allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching, describe the device variability using time series analyses to assess the “memory” effect, and employ a circuit breaker simulator to understand the formation and rupture of the percolation paths that produce the switching. We conclude that the cycle-to-cycle variability of the Au/Ti/h-BN/Au devices presented here is higher than that previously observed in Au/h-BN/Au devices, and hence, they may be useful for data encryption.

Hybrid 2D–CMOS microchips for memristive applications

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.

High‐Temporal‐Resolution Characterization Reveals Outstanding Random Telegraph Noise and the Origin of Dielectric Breakdown in h‐BN Memristors

AbstractMemristor‐based electronic memory have recently started commercialization, although its market size is small (~0.5%). Multiple studies claim their potential for hardware implementation of artificial neural networks, advanced data encryption, and high‐frequency switches for 5G/6G communication. Application aside, the performance and reliability of memristors need to be improved to increase their market size and fit technology standards. Multiple groups propose novel nano‐materials beyond phase‐change, metal‐oxides, and magnetic materials as resistive switching medium (e.g., two‐dimensional, nanowires, perovskites). However, most studies use characterization setups that are blind to critical phenomena in understanding charge transport across the devices. Here an advanced setup with high temporal resolution is used to analyze current noise, dielectric breakdown growth, and ambipolar resistive switching in memristors based on multilayer hexagonal boron nitride (h‐BN), one of the most promising novel nano‐materials for memristive applications. The random telegraph noise in pristine memristors and its evolution as the devices degrade, covering ~7 orders of magnitude in current with consistent observation, is studied. Additionally, an ambipolar switching regime with very low resistance down to 50Ω and its connection with a telegraph behavior with high/low current ratios >100, linked to a thermally‐driven disruption of a metallic nanofilament, is shown.

Large-area synthesis and transfer of multilayer hexagonal boron nitride for enhanced graphene device arrays

Multilayer hexagonal boron nitride (hBN) can be used to preserve the intrinsic physical properties of other two-dimensional materials in device structures. However, integrating the material into large-scale two-dimensional heterostructures remains challenging due to the difficulties in synthesizing high-quality large-area multilayer hBN and combining it with other two-dimensional material layers of the same scale. Here we show that centimetre-scale multilayer hBN can be synthesized on iron–nickel alloy foil by chemical vapour deposition, and then used as a substrate and as a surface-protecting layer in graphene field-effect transistors. We also develop an integrated electrochemical transfer and thermal treatment method that allows us to create high-performance graphene/hBN heterostacks. Arrays of graphene field-effect transistors fabricated by conventional and scalable methods show an enhancement in room-temperature carrier mobility when hBN is used as an insulating substrate, and a further increase—up to a value of 10,000 cm2 V−1 s−1—when graphene is encapsulated with another hBN sheet.

Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision

Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (<50 atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.

Maximizing the thermal hotspot reduction by optimizing the thickness of multilayer hBN heat spreader

This article investigates the thickness-dependent heat spreading performance of multilayer hexagonal boron nitride (hBN). The growth by a cold-wall chemical vapor deposition system results in large-area and high crystal quality of multilayer hBN. The full width at half maximum of Raman E2g peak is approximately 25 cm−1 and the bandgap is larger than 5.8 eV. Varying the growth duration from 5 to 15 min on Ni foil results in hBN thicknesses of 5–12 nm with comparable crystal quality. The electrothermal analysis using a sensitive Pt/Cu/Ti micro-coil introduces the figure of merit (FoM) of hBN as an insulator heat spreader. Fittings on FoM plots suggest that multilayer hBN with a thickness of ∼2 nm is optimum for a 25% reduction in the thermal hotspot. The initial drop in lateral thermal resistance is significant for a few nanometers-thick hBN, where a 22.5% reduction is measured from the 5.6 nm-thick hBN heat spreader. Further thickening of hBN reduces the lateral thermal resistance by approximately 0.37% nm−1. Findings from this work provide a significant contribution to the implementation of hBN as a direct contact heat spreader on the actual power devices.

Oxygen activated CVD growth of large-area multilayer h-BN on polycrystalline copper foils

The synthesis of large-area multilayer hexagonal boron nitride (h-BN) is a prerequisite for advanced 2D material-based transparent electronics. The formation of large-area h-BN domains limits the possibility of grain boundary leakage current and mechanical ruptures of h-BN layers. In this work, we report the oxygen-activated synthesis of high-quality multilayer h-BN on polycrystalline copper foils, to reduce the nucleation site density and enhance the size of h-BN domains up to ∼180 μm. The oxygen-activated synthesized h-BN domains are at least 20–30 times larger than the domains synthesized by the non-oxygen-based synthesis process.Experimental results demonstrate that the h-BN domain size does not depend on the crystal orientation of the catalyst. X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Atomic force microscopy (AFM), and Angle-resolved photoemission spectroscopy (μ-ARPES) measurements together with Raman spectroscopy indicate that the synthesized h-BN layers are of high quality.

Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform.

Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate a system comprising of multilayer hexagonal boron nitride (hBN) films contacted with silver (Ag), which can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between the high resistance state (HRS) and the low resistance state (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, two different neural networks are realized in a single CMOS compatible, 2D material platform.

Inkjet-printed h-BN memristors for hardware security.

Inkjet printing electronics is a growing market that reached 7.8 billion USD in 2020 and that is expected to grow to ∼23 billion USD by 2026, driven by applications like displays, photovoltaics, lighting, and radiofrequency identification. Incorporating two-dimensional (2D) materials into this technology could further enhance the properties of the existing devices and/or circuits, as well as enable the development of new concept applications. Along these lines, here we report an easy and cheap process to synthesize inks made of multilayer hexagonal boron nitride (h-BN)-an insulating 2D layered material-by the liquid-phase exfoliation method and use them to fabricate memristors. The devices exhibit multiple stochastic phenomena that are very attractive for use as entropy sources in electronic circuits for data encryption (physical unclonable functions [PUFs], true random number generators [TRNGs]), such as: (i) a very disperse initial resistance and dielectric breakdown voltage, (ii) volatile unipolar and non-volatile bipolar resistive switching (RS) with a high cycle-to-cycle variability of the state resistances, and (iii) random telegraph noise (RTN) current fluctuations. The clue for the observation of these stochastic phenomena resides on the unpredictable nature of the device structure derived from the inkjet printing process (i.e., thickness fluctuations, random flake orientations), which allows fabricating electronic devices with different electronic properties. The easy-to-make and cheap memristors here developed are ideal to encrypt the information produced by multiple types of objects and/or products, and the versatility of the inkjet printing method, which allows effortless deposition on any substrate, makes our devices especially attractive for flexible and wearable devices within the internet-of-things.

The Effects of Pressure and Size Parameter on the Lattice Thermal Conductivity in Multilayer Hexagonal Boron Nitride

The Effects of Pressure and Size Parameter on the Lattice Thermal Conductivity in Multilayer Hexagonal Boron Nitride