Memristors with Biomaterials for Biorealistic Neuromorphic Applications

Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications

In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.

Scaled-down ionic liquid-functionalized geopolymer memristors

Whereas most memristors are fabricated using sophisticated and expensive manufacturing methods, we recently introduced low-cost memristors constructed from sustainable, porous geopolymers (GP) at room temperature via simple casting processes. These devices exhibit resistive switching via electroosmosis and voltage-driven ion mobility inside water-filled channels within the porous material, enabling promising synaptic properties. However, GP memristors were previously fabricated at the centimeter scale, too large for space-efficient neuromorphic computing applications, and displayed limited memory retention durations due to water evaporation from the pores of the GP material. In this work, we overcome these limitations by implementing (i) an inexpensive manufacturing method that allows fabrication at micron-scale (99.998% smaller in volume than their centimeter-scale counterparts) and (ii) functionalization of GPs with EMIM+ Otf- ionic liquid (IL), which prolonged retention of the memristive switching properties by 50%. This improved class of GP-based memristors also demonstrated ideal synaptic properties in terms of paired-pulse facilitation (PPF), paired-pulse depression (PPD), and spike time dependent plasticity (STDP). These improvements pave the way for using IL-functionalized GP memristors in neuromorphic computing applications, including reservoir computing, in-memory computing, memristors crossbar arrays, and more.

Memristive Ion Dynamics to Enable Biorealistic Computing

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention. Ion-based memristive devices (IMDs), owing to the intrinsic functional similarities to their biological counterparts, hold significant promise for implementing emerging neuromorphic learning and computing algorithms. In this article, we review the fundamental mechanisms of IMDs based on ion drift and diffusion to elucidate the origins of their diverse dynamics. We then examine how these mechanisms operate within different materials to enable IMDs with various types of switching behaviors, leading to a wide range of applications, from emulating biological components to realizing specialized computing requirements. Furthermore, we explore the potential for IMDs to be modified and tuned to achieve customized dynamics, which positions them as one of the most promising hardware candidates for executing bioinspired algorithms with unique specifications. Finally, we identify the challenges currently facing IMDs that hinder their widespread usage and highlight emerging research directions that could significantly benefit from incorporating IMDs.

Optimization strategy of the emerging memristors: From material preparation to device applications

With the advent of the post-Moore era and the era of big data, advanced data storage and processing technology are in urgent demand to break the von Neumann bottleneck. Neuromorphic computing, which mimics the computational paradigms of the human brain, offers an efficient and energy-saving way to process large datasets in parallel. Memristor is an ideal architectural unit for constructing neuromorphic computing. It offers several advantages, including a simple structure, low power consumption, non-volatility, and easy large-scale integration. The hardware-based neural network using a large-scale cross array of memristors is considered to be a potential scheme for realizing the next-generation neuromorphic computing. The performance of these devices is a key to constructing the expansive memristor arrays. Herein, this paper provides a comprehensive review of current strategies for enhancing the performance of memristors, focusing on the electronic materials and device structures. Firstly, it examines current device fabrication techniques. Subsequently, it deeply analyzes methods to improve both the performance of individual memristor and the overall performance of device array from a material and structural perspectives. Finally, it summarizes the applications and prospects of memristors in neuromorphic computing and multimodal sensing. It aims at providing an insightful guide for developing the brain-like high computer chip.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

pH-Dependent Effects in Nanofluidic Memristors

Multipore membranes with nanofluidic diodes show memristive and current rectifying effects that can be controlled by the nanostructure asymmetry and ionic solution characteristics in addition to the frequency and amplitude of the electrical driving signal. Here, we show that the electrical conduction phenomena, which are modulated by the interaction between the pore surface charges and the solution mobile ions, allow for a pH-dependent neuromorphic-like potentiation of the membrane conductance by voltage pulses. Also, we demonstrate that arrangements of memristors can be employed in the design of electrochemical circuits for implementing logic functions and information processing in iontronics.

Electroactive composite biofilms integrating Kombucha, Chlorella and synthetic proteinoid Proto-Brains

In this study, we present electroactive biofilms made from a combination of Kombucha zoogleal mats and thermal proteinoids. These biofilms have potential applications in unconventional computing and robotic skin. Proteinoids are synthesized by thermally polymerizing amino acids, resulting in the formation of synthetic protocells that display electrical signalling similar to neurons. By incorporating proteinoids into Kombucha zoogleal cellulose mats, hydrogel biofilms can be created that have the ability to efficiently transfer charges, perform sensory transduction and undergo processing. We conducted a study on the memfractance and memristance behaviours of composite biofilms, showcasing their capacity to carry out unconventional computing operations. The porous nanostructure and electroactivity of the biofilm create a biocompatible interface that can be used to record and stimulate neuronal networks. In addition to in vitro neuronal interfaces, these soft electroactive biofilms show potential as components for bioinspired robotics, smart wearables, unconventional computing devices and adaptive biorobotic systems. Kombucha-proteinoids composite films are a highly customizable material that can be synthesized to suit specific needs. These films belong to a unique category of 'living' materials, as they have the ability to support cellular systems and improve bioelectronic functionality. This makes them an exciting prospect in various applications. Ongoing efforts are currently being directed towards enhancing the compositional tuning of conductivity, signal processing and integration within hybrid bioelectronic circuits.

MnO/ZnO:Zn Thin-Film Frequency Adaptive Heterostructure for Future Sustainable Memristive Systems

In recent years, advances in materials engineering based on adaptive electronics have found a new paradigm to optimize drawbacks in signal processing. A two-layer MnO/ZnO:Zn heterostructure envisioned for frequency adaptive electronic signal processing is synthesized by sputtering, where the use of internal states allows reconfigurability to obtain new operating modes at different frequency input signals. X-ray diffraction (XRD) analysis is performed on each layer, revealing a cubic structure for MnO and a hexagonal structure for ZnO:Zn with preferential growth in [111] and [002] directions, respectively. Scanning electron microscope (SEM) micrographs show that the surface of both materials is homogeneous and smooth. The thickness for each layer is determined to be approximately 106.3 nm for MnO, 119.3 nm for ZnO:Zn and 224.1 nm for the MnO/ZnO:Zn structure. An electrical characterisation with an oscilloscope and signal generator was carried out to obtain the time-response signals and current-voltage (I-V) curves, where no degradation is detected when changing frequencies within the range of 100 Hz to 1 MHz. An equivalent circuit is proposed to explain the effects in the interface. Measurements of switching speeds from high resistance state (HRS) to low resistance state (LRS) at approximately 17 ns, highlight the device's rapid adaptability, and an estimated switching ratio of approximately 2 × 104 indicates its efficiency as a memristive component. Finally, the MnO/ZnO:Zn heterojunction delivers states that are stable, repeatable, and reproducible, demonstrating how the interaction of the materials can be utilised in adaptive device applications by applying frequencies and internal states to create new and innovative design schematics, thus reducing the number of components/connections in a system for future sustainable electronics.

Memfractance of Proteinoids

Proteinoids, or thermal proteins, are amino acid polymers formed at high temperatures by nonbiological processes. The objective of this study is to examine the memfractance characteristics of proteinoids within a supersaturated hydroxyapatite solution. The ionic solution utilized for the current-voltage (I-V) measurements possessed an ionic strength of 0.15 mol/L, a temperature of 37 °C, and a pH value of 7.4. The I-V curves exhibited distinct spikes, which are hypothesized to arise from the capacitive charging and discharging of the proteinoid-hydroxyapatite media. The experimental results demonstrated a positive correlation between the concentration of proteinoids and the observed number of spikes in the I-V curves. This observation provides evidence in favor of the hypothesis that the spikes originate from the proteinoids' capacitive characteristics. The memfractance behavior exemplifies the capacity of proteinoids to retain electrical charge within the hydrated hydroxyapatite media. Additional investigation is required in order to comprehensively identify the memcapacitive phenomena and delve into their implications for models of protocellular membranes. In a nutshell, this study provides empirical support for the existence of capacitive membrane-memfractance mechanisms in ensembles of proteinoids.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Understanding the Role of M13 Bacteriophage Thin Films on a Metallic Nanostructure through a Standard and Dynamic Model

The dynamic and surface manipulation of the M13 bacteriophage via the meeting application demands the creation of a pathway to design efficient applications with high selectivity and responsivity rates. Here, we report the role of the M13 bacteriophage thin film layer that is deposited on an optical nanostructure involving gold nanoparticles/SiO₂/Si, as well as its influence on optical and geometrical properties. The thickness of the M13 bacteriophage layer was controlled by varying either the concentration or humidity exposure levels, and optical studies were conducted. We designed a standard and dynamic model based upon three-dimensional finite-difference time-domain (3D FDTD) simulations that distinguished the respective necessity of each model under variable conditions. As seen in the experiments, the origin of respective peak wavelength positions was addressed in detail with the help of simulations. The importance of the dynamic model was noted when humidity-based experiments were conducted. Upon introducing varied humidity levels, the dynamic model predicted changes in plasmonic properties as a function of changes in NP positioning, gap size, and effective index (this approach agreed with the experiments and simulated results). We believe that this work will provide fundamental insight into understanding and interpreting the geometrical and optical properties of the nanostructures that involve the M13 bacteriophage. By combining such significant plasmonic properties with the numerous benefits of M13 bacteriophage (like low-cost fabrication, multi-wavelength optical characteristics devised from a single structure, reproducibility, reversible characteristics, and surface modification to suit application requirements), it is possible to develop highly efficient integrated plasmonic biomaterial-based sensor nanostructures.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO₄) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO₄ compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

All-in-One Compression and Encryption Engine Based on Flexible Polyimide Memristor

It is anticipated that the rapid development of the Internet of Things (IoT) will improve the quality of human life. Nonetheless, large amounts of data need to be replicated, stored, processed, and shared, posing formidable challenges to communication bandwidth and information security. Herein, it is reported that polyimide (PI) threshold-switching memristors exhibit Gaussian conductance and randomly set voltage distribution with nonideal properties to create a compression and encryption engine with a single chip. The Gaussian conductance distribution is used to achieve compressed sensing (CS) to integrate encryption into compression, and the spontaneous formation of the one-time-sampling measurement matrix satisfies absolute security. Moreover, the bitstreams generated by randomly distributed set voltages are used to diffuse the ciphertext from CS to improve security. The engine is shown to be secure even if the eavesdropper knows both the plaintext and the corresponding ciphertext. It has compression performance advantages that take both efficiency and security into account. In addition, due to the superior high temperature and mechanical properties of PI, the engine can continue to function normally in harsh environments. Herein, an excellent solution is offered for ensuring the efficiency and security of IoT.

First Order Rate Law Analysis for Reset State in Vanadium Oxide Thin Film Resistive Random Access Memory Devices

In the reset state, the decay reaction mechanism and bipolar switching properties of vanadium oxide thin film RRAM devices for LRS/HRS are investigated and discussed here. To discover the properties of I-V switching curves, the first order rate law behaviors of the reset state between the resistant variety properties and the reaction time were observed. To verify the decay reaction mechanism in the reset state, vanadium oxide thin films from RRAM devices were measured by different constant voltage sampling and exhibited the same decay reaction rate constant. Finally, the electrical conduction transfer mechanism and metallic filament forming model described by I-V switching properties of the RRAM devices were proven and investigated.