Organic Memristor Research Articles

Modulating conductive filaments via thermally stable bilayer organic memristor

The basic unit of information in conductive bridge random access memory based on the redox mechanism of metal ions is physically stored in a conductive filament (CF). Therefore, the overall performance of the device is indissolubly related to the properties of such CFs, as unreliable performance often originates from unstable CFs behavior. However, accurately controlling the dissolution of CFs during device operation can be challenging due to their non-uniformity. This paper proposes a type of memristor based on a solid polymer electrolyte with a polyvinylpyrrolidone/polyvinyl alcohol composite layer structure. The properties of the composite layer are employed to regulate the number of CFs and the growth/fracture location, while the damage to the device by Joule heat is prevented. The device exhibits low operating voltage (0.5 V), stable switching conditions (2000 cycles), and a long holdup time (>3 × 104 s).

Natural Organic Honey-CNT Memristor Based Artificial Synaptic Devices for Sustainable Neuromorphic System

Neuromorphic computing is considered to have the potential to overcome the limitations of traditional von Neumann architecture due to its high efficiency, low energy consumption, and fault-tolerance. Hardware components that can emulate the synaptic plasticity of neurons, i.e. artificial synaptic devices, are required by neuromorphic systems. New devices have been examined for such components, such as phase-change artificial synapse, ferroelectric artificial synapse, and memristor synapses. Among them, memristor, a two-terminal metal-insulator-metal structure that are analogous to a biological synapse with presynaptic neuron (top electrode), postsynapticneuron (bottom electrode), and synaptic cleft (memristive film), is a promising device technology because of its tunable resistance, scalability, 3D integration compatibility, low power consumption, and relatively high speed. In contrary to inorganic materials such as metal oxides, natural organic materials have attracted interest to form the memristive layer because they are renewable, biodegradable, sustainable, biocompatible, and environmentally friendly. In this paper, honey solution embedded with carbon nanotubes (CNTs) was processed into the memristive layer by a low cost solution-based process, with synaptic plasticity of the final honey-CNT memristors characterized, including forget and relearn, spike-rate-dependent plasticity, spike-voltage-dependent plasticity, short-term to long-term memory transition, paired pulse facilitation, and spatial supra-linear summation behaviors. The successful emulation of these essential biological synaptic behaviors demonstrates the potential of honey-CNT memristors as a viable hardware component in neuromorphic computing systems.

Voltage-Driven Fluorine Motion for Novel Organic Spintronic Memristor.

Integrating tunneling magnetoresistance (TMR) effect in memristors is a long-term aspiration because it allows to realize multifunctional devices, such as multi-state memory and tunable plasticity for synaptic function. However, the reported TMR in different multiferroic tunnel junctions is limited to 100%. This work demonstrates a giant TMR of -266% in La0.6Sr0.4MnO3(LSMO)/poly(vinylidene fluoride)(PVDF)/Co memristor with thin organic barrier. Different from the ferroelectricity-based memristors, this work discovers that the voltage-driven florine (F) motion in the junction generates a huge reversible resistivity change up to 106% with nanosecond (ns) timescale. Removing F from PVDF layer suppresses the dipole field in the tunneling barrier, thereby significantly enhances the TMR. Furthermore, the TMR can be tuned by different polarizing voltage due to the strong modification of spin-polarization at the LSMO/PVDF interface upon F doping. Combining of high TMR in the organic memristor paves the way to develop high-performance multifunctional devices for storage and neuromorphic applications.

Coexistance of the Negative Photoconductance Effect and Analogue Switching Memory in the CuPc Organic Memristor for Neuromorphic Vision Computing.

A bioinspired in-sensing computing paradigm using emerging photoelectronic memristors pursues multifunctionality with low power consumption and high efficiency for processing large amounts of sensing information. An organic semiconductor memristor strategy based on the CuPc functional layer integrates a negative photoconductance (NPC) effect and an analogue switching memory (ASM) effect in the same pixel. The NPC effect, present in the pure capacitance state at low bias voltage, provides high-performance short/long-term synaptic plasticity modulable by light pulse parameters. The interface charge effect along with defeat site trapping and detrapping is responsible for the pure capacitance effect and the NPC effect, with electron tunneling and electric-field-driven band dynamics responsible for ASM. This work reveals an organic memristor approach for hardware implementation of a neuromorphic vision computing system, emulating retinal bipolar cells via light-dominated NPC and electrically induced ASM with stable, tunable conductance states.

Molecular-Potential and Redox Coregulated Cathodic Electrosynthesis toward Ionic Azulene-Based Thin Films for Organic Memristors.

Organic memristors as promising electronic units are attracting significant attention owing to their simplicity of molecular structure design. However, fabricating high-quality organic films via novel synthetic technologies and exploring unprecedented chemical structures to achieve excellent memory performance in organic memristor devices are highly challenging. In this work, we report a cathodic electropolymerization to synthesize an ionic azulene-based memristive film (PPMAz-Py+Br-) under the molecular-potential and redox coregulation. During the cathodic electropolymerization process, electropositive pyridinium salts migrate to the cathode under an electric field, undergo a reduction-coupling deprotonation reaction, and polymerize into a uniform film with a controllable thickness on the electrode surface. The prepared Al/PPMAz-Py+Br-/ITO devices not only exhibit a high ON/OFF ratio of 1.8 × 103, high stability, long memory retention, and endurance under a wide range of voltage scans, but also achieve excellent multilevel storage and history-dependent memristive performance. In addition, the devices can mimic important biosynaptic functions, such as learning/forgetting function, synaptic enhancement/inhibition, paired-pulse facilitation/depression, and spiking-rate-dependent plasticity. The tunable memristive performances are attributed to the capture of free electrons on pyridinium cations, the migration of the aluminum ions (Al3+), and the form of Al conductive filaments under voltage scans.

Organic Frameworks Memristor: An Emerging Candidate for Data Storage, Artificial Synapse, and Neuromorphic Device

AbstractMemristors have recently become powerful competitors toward artificial synapses and neuromorphic computation, arising from their structural and electrical similarity to biological synapses and neurons. From the diversity of materials, numerous organic and inorganic materials have proven to exhibit great potential in the application of memristors. Herein, this work focuses on a class of memristors based on organic frameworks (OFs) materials, and pay attention to the most advanced experimental demonstrations. First, the typical device structures and memristive switching mechanisms are introduced. Second, the latest progress of OFs‐based memristors is comprehensively summarized, including metal‐organic frameworks (MOFs), covalent organic frameworks (COFs), and hydrogen‐bonded organic frameworks (HOFs), as well as their applications in data storage, artificial synapses, and neuromorphic devices. Finally, the future challenges and prospects of OFs‐based memristors are deeply discussed.

High synaptic plasticity enabled by controlled ion migration in organic heterojunction memristors

Fabrication of organic heterojunction memristor with controlled ion migration enables precise synaptic behavior simulation.

Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation.

Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.

Formation of Cluster-Structured Metallic Filaments in Organic Memristors for Wearable Neuromorphic Systems with Bio-Mimetic Synaptic Weight Distributions.

With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural networks utilizing several types of flexible artificial synapses, it is still challenging to develop wearable systems for complex computations due to the difficulties in emulating continuous memory states in a synaptic component. In this study, polymer conductivity is analyzed as a crucial factor in determining the growth dynamics of metallic filaments in organic memristors. Moreover, flexible memristors with bio-mimetic synaptic functions such as linearly tunable weights are demonstrated by engineering the polymer conductivity. In the organic memristor, the cluster-structured filaments are grown within the polymer medium in response to electric stimuli, resulting in gradual resistive switching and stable synaptic plasticity. Additionally, the device exhibits the continuous and numerous non-volatile memory states due to its low leakage current. Furthermore, complex hardware neural networks including ternary logic operators and a noisy image recognitions system are successfully implemented utilizing the developed memristor arrays. This promising concept of creating flexible neural networks with bio-mimetic weight distributions will contribute to the development of a new computing architecture for energy-efficient wearable smart electronics.

Electron push-pull effects induced performance promotion in covalent organic polymer thin films-based memristor for neuromorphic application

Covalent organic polymer (COP) thin film-based memristors have generated intensive research interest, but the studies are still in their infancy. Herein, by controlling the content of hydroxyl groups in the aldehyde monomer, Py-COP thin films with different electronic push-pull effects were fabricated bearing distinct memory performances, where the films were prepared by the solid-liquid interface method on the ITO substrates and further fabricated as memory devices with ITO/Py-COPs/Ag architectures. The Py-COP-1-based memory device only exhibited binary memory behavior with an ON/OFF ratio of 1:101.87. In contrast, the device based on Py-COP-2 demonstrated ternary memory behavior with an ON/OFF ratio of 1:100.6:103.1 and a ternary yield of 55%. The ternary memory mechanism of the ITO/Py-COP-2/Ag memory device is most likely due to the combination of the trapping of charge carriers and conductive filaments. Interestingly, the Py-COPs-based devices can successfully emulate the synaptic potentiation/depression behavior, clarifying the programmability of these devices in neuromorphic systems. These results suggest that the electronic properties of COPs can be precisely tuned at the molecular level, which provides a promising route for designing multi-level memory devices.

Solution-Processed Organic Memristor Matrix With Behavior of Clustered Synaptic Plasticity

Solution-Processed Organic Memristor Matrix With Behavior of Clustered Synaptic Plasticity

Nonconformal Electrochemical Memristor through Vapor Phase Polymerization of Pyrrole

A nonvolatile organic synaptic memristor was fabricated utilizing a vapor phase polymerization technique for synthesizing polypyrrole. This synaptic memristor employed a bottom synaptic electrode in order to “read” the device’s conductance, termed the synaptic weight of this neuromorphic-capable device. Through a programming pulse of +1 V amplitude and a 1 s duration to the “writing” electrode, or top synaptic electrode, a resolution of 0.245 μS occurs per change for each programmed state. This organic synaptic memristor consumes a minimum programming power of 4.16 fJ mm–2 and can be further decreased via advanced patterning and layer deposition techniques. Operational applications are demonstrated for this organic synaptic memristor, including Boolean algebra (AND, NAND, OR, and NOR logic gates), as well as abacus calculations (addition, subtraction, and division).

A Universal Fabrication Strategy for High-Resolution Perovskite-Based Photodetector Arrays.

Metal halide perovskite photodetector arrays have demonstrated great potential applications in the field of integrated systems, optical communications, and health monitoring. However, the fabrication of large-scale and high-resolution device is still challenging due to their incompatibility with the polar solvents. Here, a universal fabrication strategy that utilizes ultrathin encapsulation-assisted photolithography and etching to create high-resolution photodetectors array with vertical crossbar structure is reported. This approach yields a 48 × 48 photodetector array with a resolution of 317 ppi. The device shows good imaging capability with a high on/off ratio of 3.3 × 105 and long-term working stability over 12h. Furthermore, this strategy can be applied to five different material systems, and is fully compatible with the existing photolithography and etching techniques, which are expected to have potential applications in the other high-density and solvent-sensitive devices array, including perovskite- or organic semiconductor-based memristor, light emitting diode displays, and transistors.

Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature.

Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications have been demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, we addressed the temperature issue by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 K to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism was unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behaviour of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems. This article is protected by copyright. All rights reserved.

Solution-processed small-molecular organic memristor with a very low resistive switching set voltage of 0.38 V

Organic memristors are considered to be the next-generation storage element due to their unique advantages of flexibility, transparency, and good solution processability. In this Letter, a Zn-porphyrin based small-molecular organic memristor is prepared by spin-coating with an ultralow resistive switching set voltage of 0.38 V. It is found that the zinc atom in the porphyrin molecule plays a very important role in improving the resistance switching characteristics of organic memristors. By tracking the change in oxygen valence in the vertical dimension, we demonstrate that Zn atom located in the core of porphyrin helps to enhance the oxygen ion migration across the active layer, clearly revealing the memory mechanism of low-cost solution-processed Zn-porphyrin based small-molecular organic memristors. This organic memristor shows excellent memristive performance resulting from rational material design and appropriate device structure engineering.

A Natural Organic Artificial Synaptic Device Made from a Honey and Carbon Nanotube Admixture for Neuromorphic Computing

AbstractArtificial synaptic devices are the essential hardware component in emerging neuromorphic computing systems by mimicking biological synapse and brain functions. When made from natural organic materials such as protein and carbohydrate, they have potential to improve sustainability and reduce electronic waste by enabling environmentally‐friendly disposal. In this paper, a new natural organic memristor based artificial synaptic device is reported with the memristive film processed by a honey and carbon nanotube (CNT) admixture, that is, honey‐CNT memristor. Optical microscopy, scanning electron microscopy, and micro‐Raman spectroscopy are employed to analyze the morphology and chemical structure of the honey‐CNT film. The device demonstrates analog memristive potentiation and depression, with the mechanism governing these functions explained by the formation and dissolution of conductive paths due to the electrochemical metal filaments which are assisted by CNT clusters and bundles in the honey‐CNT film. The honey‐CNT memristor successfully emulates synaptic functionalities such as short‐term plasticity and its transition to long‐term plasticity for memory rehearsal, spatial summation, and shunting inhibition, and for the first time, the classical conditioning behavior for associative learning by mimicking the Pavlov's dog experiment. All these results testify that honey‐CNT memristor based artificial synaptic device is promising for energy‐efficient and eco‐friendly neuromorphic systems.

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO4)]2/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device's conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device's synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

Li-Ion Doped Artificial Synaptic Memristor for Highly Linear Neuromorphic Computing

Linear weights modulation in neuromorphic memristor plays an important role in high-accuracy image recognition task. Herein, a Li+ doped organic artificial memristor for neuromorphic computing was proposed for linear weights update, which shows active ions diffusive dynamics as Ca2+ diffusion in biological synapse. The memristor exhibits gradual resistive switching, multi-state storage and typical synaptic behaviors. In addition, the synaptic learning capability of letter “T” was demonstrated in a memristors array. By designing consecutive pulse waveforms with enhanced amplitude, the linearity of memristor for weight update in long-term potentiation and depression (LTP/LTD) could be improved from 6.8 to 0.4. Based on the great nonlinearity factor in LTP ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {p}}={1.5}$ </tex-math></inline-formula> ) and LTD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {d}}={0.4}$ </tex-math></inline-formula> ), face recognition was achieved with high accuracy of 96% by artificial neural network consisting of ion doped memristors. The ion doped organic memristor with highly linear weights update provides guidelines for the development of bio-inspired ion diffusive neuromorphic computing system.

Multiphotoconductance Levels of the Organic Semiconductor of Polyimide-Based Memristor Induced by Interface Charges.

A memristor with Au/polyimide (PI)/Au structure is prepared by magnetron sputtering to investigate the multiphotoconductance resistive switching (RS) memory behavior. The PI-based memristor presents stable bipolar RS memory and is sensitive to visible light. Four discrete conductance states in both high-resistance state (HRS) and low-resistance state (LRS) are obtained when illuminating by 365, 550, 590, and 780 nm light. Electron trapping and detrapping from the defects distributed at interfaces and the PI switching layer are responsible for the observed RS memory behavior. The enhanced trapping and detrapping process by light illumination is responsible for the multiconductance states. This work provides the possibility for further development of neuromorphic vision sensors using an organic semiconductor-based memristor.

Low-power flexible organic memristor based on PEDOT:PSS/pentacene heterojunction for artificial synapse.

Organic synaptic memristors are of considerable interest owing to their attractive characteristics and potential applications to flexible neuromorphic electronics. In this work, an organic type-II heterojunction consisting of poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) and pentacene was adopted for low-voltage and flexible memristors. The conjugated polymer PEDOT:PSS serves as the flexible resistive switching (RS) layer, while the thin pentacene layer plays the role of barrier adjustment. This heterojunction enabled the memristor device to be triggered with low-energy RS operations (V < ± 1.0 V and I < 9.0 μA), and simultaneously providing high mechanical bending stability (bending radius of ≈2.5 mm, bending times = 1,000). Various synaptic properties have been successfully mimicked. Moreover, the memristors presented good potentiation/depression stability with a low cycle-to-cycle variation (CCV) of less than 8%. The artificial neural network consisting of this flexible memristor exhibited a high accuracy of 89.0% for the learning with MNIST data sets, even after 1,000 tests of 2.5% stress-strain. This study paves the way for developing low-power and flexible synaptic devices utilizing organic heterojunctions.