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Two-dimensional materials could play an important role in beyond-CMOS (complementary metal–oxide–semiconductor) electronics, and the development of memristors for information storage and neuromorphic computing using such materials is of particular interest. However, the creation of high-density electronic circuits for complex applications is limited due to low device yield and high device-to-device variability. Here, we show that high-density memristive crossbar arrays can be fabricated using hexagonal boron nitride as the resistive switching material, and used to model an artificial neural network for image recognition. The multilayer hexagonal boron nitride is deposited using chemical vapour deposition, and the arrays exhibit a high yield (98%), low cycle-to-cycle variability (1.53%) and low device-to-device variability (5.74%). The devices exhibit different switching mechanisms depending on the electrode material used (gold for bipolar switching and silver for threshold switching), as well as characteristics (such as large dynamic range and zeptojoule-order switching energies) that make them suited for application in neuromorphic circuits. High-density memristive crossbar arrays made from two-dimensional hexagonal boron nitride can be fabricated with a yield of 98% and used to emulate artificial neural networks.

We utilize a diffusion-controlled wet chemical etching technique to fabricate microstructures from two-dimensional HgTe/(Hg,Cd)Te-based topological insulators. For this purpose, we employ a KI: I2: HBr: H2O-based etchant. Investigation of the side profile of the etched heterostructure reveals that HgTe quantum wells protrude from the layer stack as a result of the different etch rates of the layers. This constraint poses challenges for the study of the transport properties of edge channels in HgTe quantum wells. In order to achieve a smoother side profile, we develop a novel approach to the etching process involving the incorporation of a sacrificial design element in the etch mask. This limits the flow of charge carriers to the ions in the electrolyte during the etching process. The simplicity of the method coupled with the promising results achieved thereby should make it possible for the new approach introduced here to be applied to other semiconductor heterostructures.

Advanced data encryption requires the use of true random number generators (TRNGs) to produce unpredictable sequences of bits. TRNG circuits with high degree of randomness and low power consumption may be fabricated by using the random telegraph noise (RTN) current signals produced by polarized metal/insulator/metal (MIM) devices as entropy source. However, the RTN signals produced by MIM devices made of traditional insulators, i.e., transition metal oxides like HfO and AlO, are not stable enough due to the formation and lateral expansion of defect clusters, resulting in undesired current fluctuations and the disappearance of the RTN effect. Here, the fabrication of highly stable TRNG circuits with low power consumption, high degree of randomness (even for a long string of 2 − 1 bits), and high throughput of 1 Mbit s by using MIM devices made of multilayer hexagonal boron nitride (h-BN) is shown. Their application is also demonstrated to produce one-time passwords, which is ideal for the internet-of-everything. The superior stability of the h-BN-based TRNG is related to the presence of few-atoms-wide defects embedded within the layered and crystalline structure of the h-BN stack, which produces a confinement effect that avoids their lateral expansion and results in stable operation., M.L. acknowledges generous support from the King Abdullah University of Science and Technology. This work was supported by the Ministry of Science and Technology of China (grants no. 2018YFE0100800, 2019YFE0124200), the National Natural Science Foundation of China (grants no. 61874075), the Collaborative Innovation Centre of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the 111 Project from the State Administration of Foreign Experts Affairs of China. A.A. and S.R. acknowledge the project: ModElling Charge and Heat trANsport in 2D-materIals based Composites—MECHANIC reference number: PCI2018-093120 funded by Ministerio de Ciencia, Innovación y Universidades. ICN2 is funded by the CERCA Programme/Generalitat de Catalunya and is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706). Y.S. acknowledges support from the European Union (Marie Sklodowska-Curie actions (grant no. 894840). The authors acknowledge technical advice from H.-S. Philip Wong from Stanford University and Xiaoming Xie from Chinese Academy of Sciences.

Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing. Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.

Two-dimensional (2D) material-based memristors have shown several properties that are not shown by traditional ones, such as high transparency, robust mechanical strength and flexibility, superb chemical stability, enhanced thermal heat dissipation, ultralow power consumption, coexistence of bipolar and threshold resistive switching, and ultrastable relaxation when used as electronic synapse (among others). However, several electrical performances often required in memristive applications, such as the generation of multiple stable resistive states for high-density information storage, still have never been demonstrated. Here, we present the first 2D material-based memristors that exhibit three stable and well-distinguishable resistive states. By using a multilayer hexagonal boron nitride (h-BN) stack sandwiched by multilayer graphene (G) electrodes, we fabricate 5 μm × 5 μm cross-point Au/Ti/G/h-BN/G/Au memristors that can switch between each two or three resistive states, depending on the current limitation (CL) and reset voltage used. The use of graphene electrodes plus a small cross-point structure are key elements to observe the tristate operation, which has not been observed in larger (100 μm × 100 μm) devices with an identical Au/Ti/G/h-BN/G/Au structure nor in similar small (5 μm × 5 μm) devices without graphene interfacial layers (i.e., Au/Ti/h-BN/Au). Basically, we generate an intermediate state between the high resistive state and the low resistive state (LRS), named soft-LRS (S-LRS), which may be related to the formation of a narrower conductive nanofilament across the h-BN because of the ability of graphene to limit metal penetration (at low CLs). All the 2D materials have been fabricated using the scalable chemical vapor deposition approach, which is an immediate advantage compared to other works using mechanical exfoliated 2D materials.

Two dimensional (2D) materials have been used in memristors to improve and stabilize resistive switching (RS) behavior; however, most reports used large device area $(\geq 10^{4} \mu \text{m}^{2})$ , in which currents in each resistive state may be driven by largely defective regions that are not applicable in smaller devices. Here we present the first fabrication of cross-point memristors with sizes of $5 \mu \text{m} \times 5 \mu \text{m}$ using multilayer heterogeneous van der Waals structure. We have constructed graphene/hexagonal boron nitride/graphene (G/h-BN/G) devices using Ti and Au electrodes. The devices can show two-state or tristate operation depending on the current limitation (CL) used. Similar memristors with larger size of $100 \mu \text{m} \times 100 \mu \text{m}$ do not show this behavior, indicating that miniaturization of 2D materials based memristors is key to achieve this performance.

Here we show the fabrication of small-size cross point memristors using multilayer hexagonal boron nitride (h-BN) as switching layer. Excellent bipolar resistive switching (RS) with current ON/OFF ratios >1000 and low variability is demonstrated. The devices can also be operated in threshold RS mode, and the currents in high resistive state (HRS) are the lowest ever reported (10fA @0.1V), making possible their use as selector.

Large-area multilayer hexagonal boron nitride (h-BN) dielectric stacks can be grown on different metallic substrates by chemical vapor deposition (CVD). The high temperatures used during the growth produce the polycrystallization of the metallic substrates (leading to different crystallographic orientations at the surface of each grain), which may influence the catalytic activity of the CVD process on different grains, as well as the properties of the h-BN stacks grown on them. In this work we compare the uniformity of multilayer h-BN dielectric stacks grown via CVD on two different metallic substrates: Pt and Cu. Our study reveals that using Pt substrates leads to h-BN thickness fluctuations from one Pt grain to another, while this effect remarkably reduced when the h-BN is grown on Cu substrates. Therefore, the use of Cu substrates seems to be more convenient for h-BN production and integration at the wafer level.

Electronic devices using graphene and other two dimensional (2D) materials have shown interesting performances. However, the interaction of conductive and semiconducting 2D materials with traditional dielectrics (which are necessary to build up the devices) is still very poor (i.e. the fabrication is challenging and it generates abundant interface defects). Hexagonal Boron Nitride (h-BN) is a layered insulating material that could be used as dielectric stack in 2D materials based devices, as it shows excellent insulating ability and a perfect interaction with graphene and other 2D materials. Here we show the first device-level reliability study of h-BN dielectric stacks and describe the dielectric breakdown (BD) process.

Chemical vapor deposition (CVD) is one of the most common techniques to grow large-area hexagonal boron nitride (h-BN). However, the substrate on which the h-BN is grown plays an important role in the electrical properties of this insulating material. The high temperature used during the CVD process produces the polycrystallization of the metallic substrates, which may modify the electrical properties of the h-BN/metal from one grain to another. In this work, we compare the electrical properties of CVD-grown multilayer h-BN on three different metallic substrates: Pt, Cu and Fe. This study reveals that the properties of h-BN based devices variate remarkably from one grain to another. On the contrary, this behavior is not relevant for when using Cu and Fe. On the other hand, we have also studied the resistive switching behavior in Au/Ti/h-BN/Pt, Au/Ti/h-BN/Cu, and Au/Ag/h-BN/Fe devices, and demonstrated low variability when they are grown on the same metallic grain.

Fil: Palumbo, Felix Roberto Mario. Comision Nacional de Energia Atomica. Gerencia de Area de Investigaciones y Aplicaciones No Nucleares (cac).; Argentina. Universidad Tecnologica Nacional; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina