Flexible Multistate Data Storage Devices Fabricated Using Natural Lignin at Room Temperature

Natural biomaterials for sustainable flexible neuromorphic devices

Neuromorphic electronics use neural models in hardware to emulate brain-like behavior, and provide power-efficient, extremely compact, and massively-parallel processing, so they are ideal candidates for next-generation information-processing units. However, traditional rigid neuromorphic devices are limited by their unavoidable mechanical and geometrical mismatch with human tissues or organs. At the same time, the rapid development of these electronic devices has generated a large amount of electronic waste, thereby causing severe ecological problems. Natural biomaterials have mechanical properties compatible with biological tissues, and are environmentally benign, ultra-thin, and lightweight, so use of these materials can address these limitations and be used to create next-generation sustainable flexible neuromorphic electronics. Here, we explore the advantages of natural biomaterials in simulating synaptic behavior of sustainable neuromorphic devices. We present the flexibility, biocompatibility, and biodegradability of these neuromorphic devices, and consider the potential applicability of these properties in wearable and implantable bioelectronics. Finally, we consider the challenges of device fabrication and neuromorphic system integration by natural biomaterials, then suggest future research directions.

Flexible and wearable electronic systems based on 2D hydrogel composites

Flexible electronics is a rapidly developing field of study, which integrates many other fields, including materials science, biology, chemistry, physics, and electrical engineering. Despite their vast potential, the widespread utilization of flexible electronics is hindered by several constraints, including elevated Young's modulus, inadequate biocompatibility, and diminished responsiveness. Therefore, it is necessary to develop innovative materials aimed at overcoming these hurdles and catalysing their practical implementation. In these materials, hydrogels are particularly promising owing to their three-dimensional crosslinked hydrated polymer networks and exceptional properties, positioning them as leading candidates for the development of future flexible electronics.

Hydrogel-Based Artificial Synapses for Sustainable Neuromorphic Electronics

Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.

Ionic Liquid Crystal Thin Film as Switching Layer in Nonvolatile Resistive Memory

In this study, we propose the use of an ionic liquid crystal (ILC) as a new resistive switching layer in nonvolatile resistive random-access memory (ReRAM) devices. The high-quality vacuum-deposited ILC films of 1-hexadecyl-3-methylimidazolium hexafluorophosphate ([C16mim][PF6]) enabled to demonstrate the first operation of ReRAM devices with a low set voltage of ∼1 V and stable switching behavior for up to ∼44 cycles. The key to the successful operation is that the ILC layer is in the liquid crystal phase (smectic A), where the electric double layers formed at the electrode-ILC interfaces play a significant role. The results of basic electrical properties and I-V curve fittings suggested the following operation principle: the formation and rupture of charge-composed filaments within the ILC film, where the current conduction is primarily governed by the trap charge limited current (TCLC) mechanism. These achievements will pave the way for advanced studies of ILC-based electronic devices.

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.).

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing

Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO₃ doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10⁴), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO₃ is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.

Applications of biomemristors in next generation wearable electronics

With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e. sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.

Metal Halide Perovskite-Based Memristors for Emerging Memory Applications

There is an increased demand for next-generation memory devices with high density and fast operation speed to replace conventional memory devices. Memristors are promising candidates for next-generation memory devices because of their scalability, stable data retention, low power consumption, and fast operation. Among the various types of memristors, halide perovskites exhibit potential as emerging materials for memristors by using hysteresis based on the movement of defects or ions in halide perovskites. However, research on the implementation of perovskite materials as memristors is in its early stages; some challenges and problems must be solved to enable the practical application of halide perovskites for next-generation memory devices. From this perspective, we highlight the recent progress in memristors that use halide perovskites. Moreover, we introduce a strategy to enhance the performance and analyze the operation mechanism of memory devices that use halide perovskites. Finally, we summarize the challenges in the development of device technology to use halide perovskites in next-generation memory devices.

Research Progress of Biomimetic Memristor Flexible Synapse

With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.

Tunable biological nonvolatile multilevel data storage devices

The speed with which electronic products are updated is continuously increasing. Consequently, since waste electronic products can cause serious environmental pollution, the demand for electronic products made of biological materials is becoming increasingly urgent. Although biological memristors have significant advantages, their electrical characteristics still do not meet the requirements to be used in future nonvolatile memories. Therefore, how to control their electrical characteristics has become a popular topic of research. In this study, tunable biomemristors with an Al/tussah blood (TB)-carbon nanotube (CNT)/indium tin oxide (ITO)/glass structure were fabricated. Such a device exhibits stable bipolar resistance switching behavior and good retention characteristics (10⁴ s). Experimental results show that the ON/OFF current ratio can be effectively controlled by modifying the CNT concentration in the TB-CNT composite film. Multilevel (8 levels, 3 bits per cell) storage capabilities can be achieved in the device by controlling its compliance current in order to achieve high-density storage. The resistance switching behavior originates from the formation and rupture of conductive oxygen vacancy filaments. TB is a promising natural biomaterial in the field of green electronics, and this research could blaze a new trail for the development of biological memory devices. Biomemristors with multilevel resistance states can be used as electronic synapses and are one of the choices for simulating biological synapses.

Flexible and recyclable bio-based transient resistive memory enabled by self-healing polyimine membrane

The recyclable, self-healing and easily-degradable transient electronic technology has aroused tremendous attention in flexible electronic products. However, integrating the above advantages into one single flexible electronic device is still a huge challenge. Herein, we demonstrate a flexible and recyclable bio-based memory device using fish colloid as the resistive switching layer on a polyimine substrate, which affords reliable mechanical and electrical properties under repetitive conformal deformation operation. This flexible bio-based memory device presents potential analog behaviors including memory characteristics and excitatory current response, which undergoes incremental potentiation in conductance under successive electrical pulses. Moreover, this device is expected to greatly alleviate the environmental problems caused by electronic waste. It can be decomposed rapidly in water and well recycled, which is a promising candidate for transient memories and information security. We believe that this study can provide new possibilities to the field of high-performance transient electronics and flexible resistive memory devices.

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Ternary Memristic Effect of Trilayer-Structured Graphene-Based Memory Devices

A tristable memory device with a trilayer structure utilizes poly(methyl methacrylate) (PMMA) sandwiched between double-stacked novel nanocomposite films that consist of 2-(4-tert-butylphenyl)-5-(4-biphenylyl)-1,3,4-oxadiazole (PBD) doped with graphene oxide (GO). We successfully fabricated devices consisting of single and double GO@PBD nanocomposite films embedded in polymer layers. These devices had binary and ternary nonvolatile resistive switching behaviors, respectively. Binary memristic behaviors were observed for the device with a single GO@PBD nanocomposite film, while ternary behaviors were observed for the device with the double GO@PBD nanocomposite films. The heterostructure GO@PBD/PMMA/GO@PBD demonstrated ternary charge transport on the basis of I–V fitting curves and energy-band diagrams. Tristable memory properties could be enhanced by this novel trilayer structure. These results show that the novel graphene-based memory devices with trilayer structure can be applied to memristic devices. Charge trap materials with this innovative architecture for memristic devices offer a novel design scheme for multi-bit data storage.

Green and sustainable zero-waste conversion of water hyacinth (Eichhornia crassipes) into superior magnetic carbon composite adsorbents and supercapacitor electrodes

Our facile approach converts embarrassing weed to value-added products through environmentally friendly routes towards zero-waste scheme.

A sustainable resistive switching memory device based on organic keratin extracted from hair

It is the consensus of researchers that the reuse of natural resources is an effective way to solve the problems of environmental pollution, waste and overcapacity. Moreover, compared with the case of inorganic materials, the renewability of natural biomaterials has great prominent advantages. In this study, keratin, which was first extracted from hair due to its high content in hair, was chosen as a functional layer for the fabrication of a resistance switching device with the Ag/keratin/ITO structure; in this device, a stable resistive switching memory behavior with good retention characteristic was observed. Via mechanism analysis, it is expected that there is hopping conduction at low biases, and the formation of a conductive filament occurs at high biases. Furthermore, our device exhibited a stable switching behavior with different conductive materials (Ti and FTO) as bottom electrodes, and the influence of Ag and graphite conductive nanoparticles (NPs) doped into the keratin layer on the switching performance of the device was also investigated. This study not only suggests that keratin is a potential biomaterial for the preparation of memory devices, but also provides a promising route for the fabrication of bio-electronic devices with non-toxicity, degradability, sustainability etc.

This study suggests that keratin is a potential biomaterial for the preparation of memory devices with non-toxicity, degradability and sustainability.

Phototunable Biomemory Based on Light-Mediated Charge Trap

Phototunable biomaterial-based resistive memory devices and understanding of their underlying switching mechanisms may pave a way toward new paradigm of smart and green electronics. Here, resistive switching behavior of photonic biomemory based on a novel structure of metal anode/carbon dots (CDs)-silk protein/indium tin oxide is systematically investigated, with Al, Au, and Ag anodes as case studies. The charge trapping/detrapping and metal filaments formation/rupture are observed by in situ Kelvin probe force microscopy investigations and scanning electron microscopy and energy-dispersive spectroscopy microanalysis, which demonstrates that the resistive switching behavior of Al, Au anode-based device are related to the space-charge-limited-conduction, while electrochemical metallization is the main mechanism for resistive transitions of Ag anode-based devices. Incorporation of CDs with light-adjustable charge trapping capacity is found to be responsible for phototunable resistive switching properties of CDs-based resistive random access memory by performing the ultraviolet light illumination studies on as-fabricated devices. The synergistic effect of photovoltaics and photogating can effectively enhance the internal electrical field to reduce the switching voltage. This demonstration provides a practical route for next-generation biocompatible electronics.

Biological Spiking Synapse Constructed from Solution Processed Bimetal Core-Shell Nanoparticle Based Composites

Inspired by the highly parallel processing power and low energy consumption of the biological nervous system, the development of a neuromorphic computing paradigm to mimic brain-like behaviors with electronic components based artificial synapses may play key roles to eliminate the von Neumann bottleneck. Random resistive access memory (RRAM) is suitable for artificial synapse due to its tunable bidirectional switching behavior. In this work, a biological spiking synapse is developed with solution processed Au@Ag core-shell nanoparticle (NP)-based RRAM. The device shows highly controllable bistable resistive switching behavior due to the favorable Ag ions migration and filament formation in the composite film, and the good charge trapping and transport property of Au@Ag NPs. Moreover, comprehensive synaptic functions of biosynapse including paired-pulse depression, paired-pulse facilitation, post-tetanic potentiation, spike-time-dependent plasticity, and the transformation from short-term plasticity to long-term plasticity are emulated. This work demonstrates that the solution processed bimetal core-shell nanoparticle-based biological spiking synapse provides great potential for the further creation of a neuromorphic computing system.

Ultralow Power Consumption Flexible Biomemristors

Low power consumption is the important requirement in memory devices for saving energy. In particular, improved energy efficiency is essential in implantable electronic devices for operation under a limited power supply. Here, we demonstrate the use of κ-carrageenan (κ-car) as the resistive switching layer to achieve memory that has low power consumption. A carboxymethyl (CM) group is introduced to the κ-car to increase its ionic conductivity. Ag was doped in CM:κ-car to improve the resistive switching properties of the devices. Memory devices based on Ag-doped CM:κ-car showed electroforming-free resistive switching. This device exhibited low reset voltage (∼0.05 V), fast switching speed (50 ns), and high on/off ratio (>10³) under low compliance current (10^-5 A). Its power consumption (∼0.35 μW) is much lower than those of the previously reported biomemristors. The resistive switching may be a result of an electrochemical redox process and Ag filament formation in the CM:κ-car under an electric field. This biopolymer memory can also be fabricated on flexible substrate. This study verifies the feasibility of using biopolymers for applications to future implantable and biocompatible nanoelectronics.

Recent Advances of Flexible Data Storage Devices Based on Organic Nanoscaled Materials

Following the trend of miniaturization as per Moore's law, and facing the strong demand of next-generation electronic devices that should be highly portable, wearable, transplantable, and lightweight, growing endeavors have been made to develop novel flexible data storage devices possessing nonvolatile ability, high-density storage, high-switching speed, and reliable endurance properties. Nonvolatile organic data storage devices including memory devices on the basis of floating-gate, charge-trapping, and ferroelectric architectures, as well as organic resistive memory are believed to be favorable candidates for future data storage applications. In this Review, typical information on device structure, memory characteristics, device operation mechanisms, mechanical properties, challenges, and recent progress of the above categories of flexible data storage devices based on organic nanoscaled materials is summarized.

Memristic Characteristics from Bistable to Tristable Memory with Controllable Charge Trap Carbon Nanotubes

The incorporation of the one-dimensional carbon nanomaterial carbon nanotubes (CNTs) in poly(methyl methacrylate) (PMMA) was found to successfully develop a resistive switching. It implements memristic characteristics which shift from bistable to tristable memory. The localized current pathways in the organic nanocomposite layers for each intermediate resistive state (IRS) are attributed to the trapping mechanism consistent with the fluorescent measurements. Multi-bit organic memories have attracted considerable interest, which provide an effective way to increase the memory density per unit cell area. This study will be useful for the development and tuning of multi-bit storable organic nanocomposite memory device systems.

Electric Field Manipulated Multilevel Magnetic States Storage in FePt/(011) PMN-PT Heterostructure

In the current information society, the realization of a magnetic storage technique with energy-efficient design and high storage density is greatly desirable. Here, we demonstrate that, without bias magnetic field, different values of remanent magnetization (M_r) can be obtained in a FePt/0.7Pb(Mg_1/3Nb_2/3)O₃-0.3PbTiO₃ (PMN-PT) heterostructure by applying a unipolar electric field across the substrate. These multilevel magnetic signals can serve as writing data bits in a storage device, which remarkably increases the storage density. As for the data reading, these multilevel M_r values can be read nondestructively and distinguishably using a commercial giant magnetoresistance magnetic sensor by converting the magnetic signal to voltage signal. Furthermore, these multilevel voltage signals show good retention and switching property, which enables promising applications in electric-writing magnetic-reading memory devices with low power consumption and high storage density.

Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials

Emulation of biological synapses that perform memory and learning functions is an essential step toward realization of bioinspired neuromorphic systems. Artificial synaptic devices have been developed based mostly on inorganic materials and conventional semiconductor device fabrication processes. Here, we propose flexible biomemristor devices based on lignin by a simple solution process. Lignin is one of the most abundant organic polymers on Earth and is biocompatible, biodegradable, as well as environmentally benign. This memristor emulates several essential synaptic behaviors, including analog memory switching, short-term plasticity, long-term plasticity, spike-rate-dependent plasticity, and short-term to long-term transition. A flexible lignin-based artificial synapse device can be operated without noticeable degradation under mechanical bending test. These results suggest lignin can be a promising key component for artificial synapses and flexible electronic devices.

Controllable Organic Resistive Switching Achieved by One-Step Integration of Cone-Shaped Contact

Conductive filaments (CFs)-based resistive random access memory possesses the ability of scaling down to sub-nanoscale with high-density integration architecture, making it the most promising nanoelectronic technology for reclaiming Moore's law. Compared with the extensive study in inorganic switching medium, the scientific challenge now is to understand the growth kinetics of nanoscale CFs in organic polymers, aiming to achieve controllable switching characteristics toward flexible and reliable nonvolatile organic memory. Here, this paper systematically investigates the resistive switching (RS) behaviors based on a widely adopted vertical architecture of Al/organic/indium-tin-oxide (ITO), with poly(9-vinylcarbazole) as the case study. A nanoscale Al filament with a dynamic-gap zone (DGZ) is directly observed using in situ scanning transmission electron microscopy (STEM) , which demonstrates that the RS behaviors are related to the random formation of spliced filaments consisting of Al and oxygen vacancy dual conductive channels growing through carbazole groups. The randomicity of the filament formation can be depressed by introducing a cone-shaped contact via a one-step integration method. The conical electrode can effectively shorten the DGZ and enhance the localized electric field, thus reducing the switching voltage and improving the RS uniformity. This study provides a deeper insight of the multiple filamentary mechanisms for organic RS effect.