Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology

In-Plane Polarization-Triggered WS2-Ferroelectric Heterostructured Synaptic Devices

To date, various kinds of memristors have been proposed as artificial neurons and synapses for neuromorphic computing to overcome the so-called von Neumann bottleneck in conventional computing architectures. However, related working principles are mostly ascribed to randomly distributed conductive filaments or traps, which usually lead to high stochasticity and poor uniformity. In this work, a heterostructure with a two-dimensional WS2 monolayer and a ferroelectric PZT film were demonstrated for memristors and artificial synapses, triggered by in-plane ferroelectric polarization. It is noted that the properties of the WS2/PZT heterostructures, including photoluminescence (PL) and conductivity, can be effectively tuned by in-plane polarization. In contrast to conventional memristors, the resistance switch of our memristors relies on the dynamic regulation of Schottky barriers at the WS2/metal contacts by ferroelectric polarization. PL characterizations verified the existence of lateral fields inside the WS2 originating from the polarization of the PZT. In particular, such memristors can emulate neuromorphic functions, including threshold-driven spiking, excitatory postsynaptic current, paired-pulse promotion (PPF), and so on. The results indicate that the WS2/PZT heterostructures with in-plane polarization are promising for the hardware implementation of artificial neural networks.

Ion-Induced Phase Changes in 2D MoTe2 Films for Neuromorphic Synaptic Device Applications

Two-dimensional molybdenum ditelluride (2D MoTe2) is an interesting material for artificial synapses due to its unique electronic properties and phase tunability in different polymorphs 2H/1T'. However, the growth of stable and large-scale 2D MoTe2 on a CMOS-compatible Si/SiO2 substrate remains challenging because of the high growth temperature and impurity-involved transfer process. We developed a large-scale MoTe2 film on a Si/SiO2 wafer by simple sputtering followed by lithium-ion intercalation and applied it to artificial synaptic devices. The Al2O3 passivation layer allows us to develop a stable 1T'-MoTe2 phase by preventing Te segregation caused by the weak bonding between Mo and Te atoms during lithiation. The lithiated MoTe2 film exhibits excellent synaptic behavior such as long-term potentiation/depression, a high Ion/Ioff ratio (≈103) at lower sweep voltage, and long-term retention. The in situ Raman analysis along with a systematic microstructural analysis reveals that the intercalated Li ion can provide an efficient pathway for conducting filament formation.

Printed High-Entropy Prussian Blue Analogs for Advanced Non-Volatile Memristive Devices

Non-volatile memristors dynamically switch between high (HRS) and low resistance states (LRS) in response to electrical stimuli, essential for electronic memories, neuromorphic computing, and artificial intelligence. High-entropy Prussian blue analogs (HE-PBAs) are promising insertion-type battery materials due to their diverse composition, high structural integrity, and favorable ionic conductivity. This work proposes a non-volatile, bipolar memristor based on HE-PBA. The device, featuring an active layer of HE-PBA sandwiched between Ag and ITO electrodes, is fabricated by inkjet printing and microplotting. The conduction mechanism of the Ag/HE-PBA/ITO device is systematically investigated. The results indicate that the transition between HRS and LRS is driven by an insulating-metallic transition, triggered by extraction/insertion of highly mobile Na+ ions upon application of an electric field. The memristor operates through a low-energy process akin to Na+ shuttling in Na-ion batteries rather than depending on formation/rupture of Ag filaments. Notably, it showcases promising characteristics, including non-volatility, self-compliance, and forming-free behavior, and further exhibits low operation voltage (VSET = -0.26 V, VRESET = 0.36 V), low power consumption (PSET = 26 µW, PRESET = 8.0 µW), and a high ROFF/RON ratio of 104. This underscores the potential of high-entropy insertion materials for developing printed memristors with distinct operation mechanisms.

Memristive properties and synaptic plasticity in substituted pyridinium iodobismuthates

This study explores the impact of organic cations in bismuth iodide complexes on their memristive behavior in metal-insulator-metal (MIM) type thin-layer devices. The presence of electron-donating and electron withdrawing functional groups (-CN, -CH3, -NH2, and -N(CH3)2) on pyridinium cations induces morphological alterations in crystals, thus influencing the electronic or ionic conductivity of devices comprising sandwiched thin layers (thickness = 200 nm ± 50) between glass/ITO as bottom electrode (∼110 nm) and copper (∼80 nm) as the top electrode. It was found that the current-voltage (I-V) scans of the devices reveal characteristic pinched hysteresis loops, a distinct signature of memristors. The working voltage windows are significantly influenced by both the types of cation and the dimensionality of ionic fragments (0D or 1D) in the solid-state form. Additionally, the temperature alters the surface area of the I-V loops by affecting resistive switching mechanisms, corresponding log-log plots at three temperatures (-30 °C, room temperature and 150 °C) are fully studied. Given that a memristor can operate as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigates long-term depression, potentiation, and spike-time-dependent plasticity-a specific form of the Hebbian learning rule-to mimic biologically synaptic plasticity. Different polar pulses, such as triangle, sawtooth, and square waveforms were employed to generate Hebbian learning rules. The research demonstrates how the shape of the applied pulse series, achieved by overlapping pre- and post-pulses at different time scales, in association with the composition and dimensionality of ionic fragments, lead to changes in the synaptic weight percentages of the devices.

High-Density, Nonvolatile, Flexible Multilevel Organic Memristor Using Multilayered Polymer Semiconductors

Nonvolatile organic memristors have emerged as promising candidates for next-generation electronics, emphasizing the need for vertical device fabrication to attain a high density. Herein, we present a comprehensive investigation of high-performance organic memristors, fabricated in crossbar architecture with PTB7/Al-AlOx-nanocluster/PTB7 embedded between Al electrodes. PTB7 films were fabricated using the Unidirectional Floating Film Transfer Method, enabling independent uniform film fabrication in the Layer-by-Layer (LbL) configuration without disturbing underlying films. We examined the charge transport mechanism of our memristors using the Hubbard model highlighting the role of Al-AlOx-nanoclusters in switching-on the devices, due to the accumulation of bipolarons in the semiconducting layer. By varying the number of LbL films in the device architecture, the resistance of resistive states was systematically altered, enabling the fabrication of novel multilevel memristors. These multilevel devices exhibited excellent performance metrics, including enhanced memory density, high on-off ratio (>108), remarkable memory retention (>105 s), high endurance (87 on-off cycles), and rapid switching (∼100 ns). Furthermore, flexible memristors were fabricated, demonstrating consistent performance even under bending conditions, with a radius of 2.78 mm for >104 bending cycles. This study not only demonstrates the fundamental understanding of charge transport in organic memristors but also introduces novel device architectures with significant implications for high-density flexible applications.

Graphene oxide-DNA/graphene oxide-PDDA sandwiched membranes with neuromorphic function

The behavior of polyelectrolytes in confined spaces has direct relevance to the protein mediated ion transport in living organisms. In this paper, we govern lithium chloride transport by the interface provided by polyelectrolytes, polycation, poly(diallyldimethylammonium chloride) (PDDA) and, polyanion, double stranded deoxyribonucleic acid (dsDNA), in confined graphene oxide (GO) membranes. Polyelectrolyte-GO interfaces demonstrate neuromorphic functions that were successfully applied with nanochannel ion interactions contributed, resulting in ion memory effects. Excitatory and inhibitory post-synaptic currents were tuned continuously as the number of pulses applied increased accordingly, increasing decay times. Furthermore, we demonstrated the short-term memory of a trained vs untrained device in computation. On account of its simple and safe production along with its robustness and stability, we anticipate our device to be a low dimensional building block for arrays to embed artificial neural networks in hardware for neuromorphic computing. Additionally, incorporating such devices with sensing and actuating parts for a complete feedback loop produces robotics with its own ability to learn by modifying actuation based on sensing data.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Element Regulation and Dimensional Engineering Co-Optimization of Perovskite Memristors for Synaptic Plasticity Applications

Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.

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.

Bipolar Resistive Switching in 2D MoSe2 Grown by Atmospheric Pressure Chemical Vapor Deposition

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are highly promising nanomaterials for various electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. 2D MoSe2 stands out for having high electrical conductivity, charge carrier mobility, and melting point. While these features make it particularly appropriate as a switching layer in memristive devices, reliable and scalable production of large-area 2D MoSe2 still represents a challenge. In this study, we manufacture 2D MoSe2 films by atmospheric-pressure chemical vapor deposition and investigate them on the atomic scale. We selected and transferred MoSe2 bilayer to serve as a switching layer between asymmetric Au-Cu electrodes in miniaturized crossbar vertical memristors. The electrochemical metallization devices showed forming-free, bipolar resistive switching at low voltages, with clearly identifiable nonvolatile states. Other than low-power neuromorphic computing, low switching voltages approaching the range of biological action potentials could unlock hybrid biological interfaces.

Resistive Switching in Bigraphene/Diamane Nanostructures Formed on a La3Ga5SiO14 Substrate Using Electron Beam Irradiation

Memristors, resistive switching memory devices, play a crucial role in the energy-efficient implementation of artificial intelligence. This study investigates resistive switching behavior in a lateral 2D composite structure composed of bilayer graphene and 2D diamond (diamane) nanostructures formed using electron beam irradiation. The resulting bigraphene/diamane structure exhibits nonlinear charge carrier transport behavior and a significant increase in resistance. It is shown that the resistive switching of the nanostructure is well controlled using bias voltage. The impact of an electrical field on the bonding of diamane-stabilizing functional groups is investigated. By subjecting the lateral bigraphene/diamane/bigraphene nanostructure to a sufficiently strong electric field, the migration of hydrogen ions and/or oxygen-related groups located on one or both sides of the nanostructure can occur. This process leads to the disruption of sp3 carbon bonds, restoring the high conductivity of bigraphene.

A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning

The memristor has been extensively used to facilitate the synaptic online learning of brain-inspired spiking neural networks (SNNs). However, the current memristor-based work can not support the widely used yet sophisticated trace-based learning rules, including the trace-based Spike-Timing-Dependent Plasticity (STDP) and the Bayesian Confidence Propagation Neural Network (BCPNN) learning rules. This paper proposes a learning engine to implement trace-based online learning, consisting of memristor-based blocks and analog computing blocks. The memristor is used to mimic the synaptic trace dynamics by exploiting the nonlinear physical property of the device. The analog computing blocks are used for the addition, multiplication, logarithmic and integral operations. By organizing these building blocks, a reconfigurable learning engine is architected and realized to simulate the STDP and BCPNN online learning rules, using memristors and 180 nm analog CMOS technology. The results show that the proposed learning engine can achieve energy consumption of 10.61 pJ and 51.49 pJ per synaptic update for the STDP and BCPNN learning rules, respectively, with a 147.03× and 93.61× reduction compared to the 180 nm ASIC counterparts, and also a 9.39× and 5.63× reduction compared to the 40 nm ASIC counterparts. Compared with the state-of-the-art work of Loihi and eBrainII, the learning engine can reduce the energy per synaptic update by 11.31× and 13.13× for trace-based STDP and BCPNN learning rules, respectively.

Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory

A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7  A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).

Evaluating charge-type of polyelectrolyte as dielectric layer in memristor and synapse emulation

Based on credible advantages, organic materials have received more and more attention in memristor and synapse emulation. In particular, an implementation of the ionic pathway as a dielectric layer is essential for organic materials used as building blocks of memristor and artificial synaptic devices. Herein, we describe an evaluation of the use of positive and negative polyelectrolytes as dielectric layers for a memristor with calcium ion (Ca²⁺) doping. The device based on a negative polyelectrolyte shows the potential to obtain an excellent resistive switching performance and synapse functionality, especially in the transformation behaviours from short-term plasticity (STP) to long-term plasticity (LTP) in both the potentiation and depression processes, which were comparable to the perfomrmance obtained with a positive polyelectrolyte. The mechanism of electrical resistance transition and synaptic function can be attributed to the migration of the doped Ca²⁺ and the ionic functional groups of polyelectrolyte, which result in the formation and vanishing filament-like Ca²⁺ flux.

Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude

Neuromorphic systems built from memristors that emulate bioelectrical information processing in the brain may overcome the limitations of traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50-120 mV at least 10 times lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude can thus advance neuromorphic engineering and bio-emulated integration. This review aims to provide a timely update on the effort and progress in this burgeoning research direction, covering the aspects of device material composition, performance, working mechanism, and potential application.

Salt-Assisted Low-Temperature Growth of 2D Bi2 O2 Se with Controlled Thickness for Electronics

Bi₂ O₂ Se is the most promising 2D material due to its semiconducting feature and high mobility, making it propitious channel material for high-performance electronics that demands highly crystalline Bi₂ O₂ Se at low-growth temperature. Here, a low-temperature salt-assisted chemical vapor deposition approach for growing single-domain Bi₂ O₂ Se on a millimeter scale with thicknesses of multilayer to monolayer is presented. Because of the advantage of thickness-dependent growth, systematical scrutiny of layer-dependent Raman spectroscopy of Bi₂ O₂ Se from monolayer to bulk is investigated, revealing a redshift of the A_1g mode at 162.4 cm^-1 . Moreover, the long-term environmental stability of ≈2.4 nm thick Bi₂ O₂ Se is confirmed after exposing the sample for 1.5 years to air. The backgated field effect transistor (FET) based on a few-layered Bi₂ O₂ Se flake represents decent carrier mobility (≈287 cm²  V^-1 s^-1 ) and an ON/OFF ratio of up to 10⁷ . This report indicates a technique to grow large-domain thickness controlled Bi₂ O₂ Se single crystals for electronics.

Electrochemical Resistive Switching in Nanoporous Hybrid Films by One-Step Molecular Layer Deposition

An organic-inorganic hybrid resistive random-access memory based on a nanoporous zinc-based hydroquinone (Zn-HQ) thin film has been constructed with a Pt/Zn-HQ/Ag sandwich structure. The porous Zn-HQ functional layer was directly fabricated by a one-step molecular layer deposition. These Pt/Zn-HQ/Ag devices show a typical electroforming-free bipolar resistive switching characteristic with lower operation voltages and higher on/off ratio above 10². Our nanoporous hybrid devices can also realize multilevel storage capability and exhibit excellent endurance/retention properties. The connection and disconnection of Ag conductive filaments in nanoporous Zn-HQ thin film follow the electrochemical metallization mechanism. Our computational simulations confirm that the existence of nanopores in Zn-HQ thin films facilitates the Ag filament formation, contributing to the high performance of our hybrid devices.

Actual origin and precise control of asymmetrical hysteresis in an individual CH3NH3PbI3 micro/nanowire for optical memory and logic operation

Although CH₃NH₃PbI₃ can present an excellent photoresponse to visible light, its application in solar cells and photodetectors is seriously hindered due to hysteresis behaviour. Moreover, for its origin, there exist different opinions. Herein, we demonstrate a route to realize precise control for the electrical transport of a single CH₃NH₃PbI₃ micro/nanowire by constructing a two-terminal device with asymmetric Ag and C electrodes, and its hysteresis can be clearly identified as a synergistic effect of the redox reaction at the interface of the Ag electrode and the injection and ejection of holes in the interfacial traps of the C electrode rather than its bulk effect. The device can show superior bias amplitude and illumination intensity dependence of hysteresis loops with typical bipolar resistive switching features. Thus, an excellent multilevel nonvolatile optical memory can be effectively realized by the modulation of the illumination and bias, and moreover a logic OR gate operation can be successfully implemented with voltage and illumination as input signals as well. This work clearly reveals and provides a new insight of hysteresis origin that can be attributed to a synergistic effect of two asymmetrical electrode interfaces, and therefore precisely controlling its electrical transport to realize an outstanding application potential in multifunctional devices integrated with optical nonvolatile memory and logic OR gate operation.

Low-Power Memristor Based on Two-Dimensional Materials

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.