Real-time edge artificial intelligence (AI) demands memory elements that are not only energy-efficient and multifunctional, but also compact, tunable, and integrable with flexible substrates. Planar memory architecture offers distinct advantages for neuromorphic computing, including surface accessibility, facile fabrication, and seamless integration with flexible substrates, making it ideal for next-generation synaptic hardware. Traditional metal oxide-based memristors often fail to meet all these requirements simultaneously due to their rigid architecture and limited material versatility. Herein, we present a planar Ti³C²T^x-MXene-based memristor (PMX-memristor) fabricated on a flexible cyclic olefin copolymer (COC) substrate, constituting the first fully planar MXene-based resistive device reported to date. The planar architecture exposes the active MXene channel, which enables direct surface inspection and functionalization while delivering robust analog switching. By tuning the voltage amplitude, the device operates in two modes: (i) a volatile regime based on valence change dynamics with transient conductance states, and (ii) a non-volatile regime driven by voltage-induced Ti→TiOx transformation, supporting eight distinct resistance levels. Detailed EDX and XPS analyses, performed before and after electrical stress, confirm the voltage-induced oxidation pathway that underpins this dual-mode behavior. The memristor’s eight-level precision enables compact 9-bit weight encoding using 3×3-bit multi-level cells in crossbar arrays, reducing area and energy compared to binary implementations. We demonstrate end-to-end deployment of these devices in spiking neural networks for real-time classification of neuromorphic vision datasets, showcasing high-performance, task-relevant learning capabilities on benchmarks such as N-MNIST and DVS-Gesture. These results underscore the potential of the designed PMX-memristor for voltage-controlled, neuromorphic edge computing and provide direct surface accessibility for functionalization and potential bio-interfacing for next-generation smart wearables.

Introduction: In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive behavior. This positioned memristors as strong candidates for implementing biologically accurate artificial neurons. Memristor-based brain simulations offer advantages in energy efficiency, scalability, and compactness, benefiting fields such as soft robotics, embedded systems, and neuroprosthetics. Methods: Previous approaches used current-controlled Mott memristors, which poorly matched the voltage-controlled nature of ion channels. This study employs volatile, oxide-based memristors that leverage electric-field-driven oxygen-vacancy migration to emulate voltage-dependent channel behavior. We selected candidate WOx and NbOx memristors and modeled their dynamics to verify performance as Hodgkin-Huxley potassium channels. Results: The device exhibits sigmoidal gating and voltage-dependent time constants consistent with the theoretical model. By scaling the passive circuitry around the memristors, we show that they capture the essential mechanisms of potassium ion-channels, although spike height is reduced due to strong non-linear voltage-dependence. Still, by cascading multiple compartments, typical spike propagation is retained. Discussion: This is the first demonstration of a voltage-controlled memristor replicating the Hodgkin-Huxley potassium channel, validating its potential for more efficient brain simulation hardware.

Memristor technology has shown great promise for energy-efficient computing [1] , though it is still facing many challenges [1 , 2]. For instance, the required additional costly electroforming to establish conductive pathways is seen as a significant drawback as it contributes to power and area overheads, and limited device endurance. In this work, we propose a novel forming-free HfO₂ -based ReRAM device with low operating voltages , multi-level capability , and less sensitivity to device-to-device (D2D) and cycle-to-cycle (C2C) variations. The device is fabricated using CMOS-compatible processes, excluding the undesirable complex steps mandatory to manufacture the state-of-the-art forming-free devices [3, 4, 5]. This is accomplished by utilizing the desirable formation energy of Pd-O bonds [6, 7], which creates conducting paths at room temperature while maintaining the analog switching ability of the devices. The proposed ReRAM device holds a great value for dense memories and energy-efficient compute architectures.

Environmentally friendly humidity sensors with high sensing performance are considered crucial components for various wearable electronic devices. We developed a rapid-response and durable Paper Cellulose Fiber/Graphene Oxide Matrix (PCFGOM) humidity sensor using an all-carbon functional material. The fabricated sensor demonstrated a high sensitivity to humidity through an electrical impedance measurement, with an increase in response to humidity ranging from 10% to 90% at 1 kHz and 10 kHz, respectively, along with a response time of 1.2 s and a recovery time of 0.8 s. The stability of the sensor was also examined, with consistent performance over a period of 24 h. This novel sensor was employed in several applications, including non-contact proximity sensing, environmental humidity detection, and human respiration detection, to showcase its potential. Moreover, this work represents a significant milestone in developing inexpensive and eco-friendly humidity sensors, given the abundance of paper and graphene in nature and their biocompatibility.

Recently, phase change chalcogenides, such as monochalcogenides, are reported as switching materials for conduction-bridge-based memristors. However, the switching mechanism focused on the formation and rupture of an Ag filament during the SET and RESET, neglecting the contributions of the phase change phenomenon and the distribution and re-distribution of germanium vacancies defects. The different thicknesses of germanium telluride (GeTe)-based Ag/GeTe/Pt devices are investigated and the effectiveness of phase loops and defect loops future application in neuromorphic computing are explored. GeTe-based devices with thicknesses of 70, 100, and 200 nm, are fabricated and their electrical characteristics are investigated. Highly reproducible phase change and defect-based characteristics for a 100 nm-thick GeTe device are obtained. However, 70 and 200 nm-thick devices are unfavorable for the reliable memory characteristics. Upon further analysis of the Ag/GeTe/Pt device with 100 nm of GeTe, it is discovered that a state-of-the-art dependency of phase loops and defect loops exists on the starting and stopping voltage sweeps applied on the top Ag electrode. These findings allow for a deeper understanding of the switching mechanism of monochalcogenide-based conduction-bridge memristors.

Abstract: The development of flexible memristor (MR) devices is a nascent research area with great potential to revolutionize wearable electronics. A few flexible MR devices with proven functionality and reliability have been introduced in the literature. This article describes the development of a novel paper MR device, named PrMem, employing a novel low-cost fabrication technique. PrMem consists of three layers: a top electrode, an active layer, and a bottom electrode. All three layers are made of the same materials, specifically, cellulose and reduced graphene oxide with different concentrations. Detailed I–V measurements are carried out to verify the resistive switching property of PrMem. Because the device is made entirely of paper, it is hydrophilic, which means that liquids may flow freely through its porous structure. This simple capillary action eliminates the need for additional mechanical pumping structures, making PrMem a promising candidate for a variety of applications. We are the first to report on the significant potential of flexible GO-based paper MR devices for emerging wearable electronics and sensing applications. Using only paper to make MR has the advantages of being low cost, flexible, effective, biocompatible, and conveniently disposable. Impact statement: This article describes the first paper-based flexible graphene oxide memristor device with vertical stack configuration. Developed and reported is a novel, cost-effective fabrication technique for paper electronics. By oxidizing reduced graphene oxide, the fully paper device achieves memristive switching behavior. The devices exhibit analog switching behavior as they undergo a transition from a state of low resistance to a state of high resistance. The device’s resistance naturally returns to its initial state without the need for a RESET voltage. The reported findings open a new frontier for research into the use of paper-based memristor devices in a wide range of applications. Graphical abstract: [Figure not available: see fulltext.].

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device’s conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems.

In recent years, there has been a growing interest in investigating the potential of emerging memristor (MR) devices for gas sensing applications, particularly at room temperature. This article reports on a planar Au/reduced graphene oxide (rGO)/Au memristive hydrogen sensor, fabricated on a cost-effective cyclic olefin copolymer (COC) substrate, and utilizing the rGO green carbon material as its active sensing element. The sensor's performance is evaluated using two different testing modes: conventional chemiresistive testing under a constant voltage bias (CVB) and voltage pulse (VP) modes. The CVB mode demonstrates high repeatability, selectivity, response time, and recovery time, indicating the sensor's reliable gas sensing capabilities. In addition, the VP mode significantly enhances the sensor's relative percentage response, indicating its potential for improved gas sensing performance. To optimize the sensor's response, the impact of hydrogen exposure on the MR resistive switching is studied, revealing that the effect is contingent on the VP amplitude. Specifically, gas-enhanced resistive switching is achieved at lower voltage levels, whereas at higher voltage levels, gas exposure slows down the rate of resistive switching. Consequently, voltage-pulse testing is conducted at two voltage magnitudes, low (2.5 V) and high (4.5 V), and the sensor's response is enhanced from 0.5% under CVB mode to 786% under VP mode.

Due to their stability, single-walled carbon nanotubes (SWCNTs) have been used for multiple applications in the semiconductor industry. Herein, we report the humidity sensing capability of SWCNTs by comparing the different densities of SWCNTs dispersed on the sensing area of the planner sensor device. Three different humidity sensors have been fabricated by preparing three different densities of CNTs diluted in deionized water and drop cast on the channel of 100 μm width between 2mm x 1mm gold electrodes. It is observed that for very low density and high density of SWCNTs sensing layer, the sensing behavior either lacks in the detection range and response time, respectively. However, we found the optimized density of SWCNTs in deionized water for a highly sensitive, fast, and high-range humidity sensor for the optimized density of SWCNTs in deionized water.

The advances in material science along with the development of fabrication techniques have enabled the realization of thin-film-based electronics on active substrates. This has substantially enhanced and supported the deployment of electronic devices in several emerging applications with flexible functionality. In this work, we report a novel fabrication of graphene oxide (GO)- based memristor devices on an active/shrinkable substrate. The standard lithography process is used to fabricate planar Au-rGO-Au devices on a polymer substrate that has the ability to shrink at a certain temperature (i.e., 170 °C). Upon heating, the devices are shrunk to 50% of their original size. A detailed electrical characterization has been carried out to study the switching behavior of the fabricated devices before and after shrinking. The results prove that upon shrinking, the device preserves its switching ability with enhanced electrical parameters (i.e., switching voltage). Also, material characterization performed for the deposited GO on the active substrate shows improved properties of the GO film due to the enhanced arrangement of GO flakes after shrinking. The novel approach proposed in this work provides new insights into and offers the ability to scale thin-film electronics postfabrication and thus overcome some of the device scaling challenges due to manufacturing limitations. It also unfolds a new path for the realization of GO-based electronic devices with improved electrical properties, which is a crucial aspect of the development of highly flexible and lightweight green electronics.

Random spray retinex (RSR) is an effective image enhancement algorithm owing to its effectiveness in improving the image quality. However, the computing complexity of the algorithm, the required hardware resources, and memory access hamper its deployment in many application scenarios, for instance, in IoT systems with limited hardware resources. With the rise of artificial intelligence (AI), the use of image enhancement has become essential for improving the performance of many emerging applications. In this paper, we propose the use of RSR as a preprocessing filter before the task of semantic segmentation of low-quality urban road scenes. Using the publicly available Cityscapes dataset, we compared the performance of a pre-trained deep semantic segmentation network on dark and noisy images with that of RSR preprocessed images. Our findings confirm the effectiveness of RSR in improving segmentation accuracy. In addition, to address the computational complexity and suitability of edge devices, we propose a novel and efficient implementation of RSR using resistive random access memory (RRAM) technology. This architecture provides highly parallel analog in-memory computing (IMC) capabilities. A detailed, efficient, and low-latency implementation of RSR using RRAM-CMOS technology is described. The design was verified using SPICE simulations with measured data from the fabricated RRAM and 65 nm CMOS technologies. The approach presented here represents an important step towards a low-complexity, real-time hardware-friendly architecture and the design of retinex algorithms for edge devices.

Physical unclonable functions (PUF) are cryptographic primitives employed to generate true and intrinsic randomness which is critical for cryptographic and secure applications. Thus, the PUF output (response) has properties that can be utilized in building a true random number generator (TRNG) for security applications. The most popular PUF architectures are transistor-based and they focus on exploiting the uncontrollable process variations in conventional CMOS fabrication technology. Recent development in emerging technology such as memristor-based models provides an opportunity to achieve a robust and lightweight PUF architecture. Memristor-based PUF has proven to be more resilient to attacks such as hardware reverse engineering attacks. In this paper, we design a lightweight and low-cost memristor PUF and verify it against cryptographic randomness tests achieving a unique, reliable, irreversible random sequence output. The current research demonstrates the architecture of a low-cost, high endurance Cu/HfO₂/ p^{+ +}Si memristor-based PUF (MR-PUF) which is compatible with advanced CMOS technologies. This paper explores the 15 NIST cryptographic randomness tests that have been applied to our Cu/HfO₂/ p^{+ +}Si MR-PUF. Moreover, security properties such as uniformity, uniqueness, and repeatability of our MR-PUF have been tested in this paper and validated. Additionally, this paper explores the applicability of our MR-PUF on block ciphers to improve the randomness achieved within the encryption process. Our MR-PUF has been used on block ciphers to construct a TRNG cipher block that successfully passed the NIST tests. Additionally, this paper investigated MR-PUF within a new authenticated key exchange and mutual authentication protocol between the head-end system (HES) and smart meters (SM)s in an advanced metering infrastructure (AMI) for smartgrids. The authenticated key exchange protocol utilized within the AMI was verified in this paper to meet the essential security when it comes to randomness by successfully passing the NIST tests without a post-processing algorithm.

This work reports on the fabrication of a novel planar reduced graphene oxide (rGO) memristor (MR) device. For the first time in the literature, the MR tunable resistive switching behavior is controlled by the GO reduction time at a constant temperature. The device is fabricated using standard microfabrication techniques on a flexible cyclic olefin copolymer substrate (COC). Thermal reduction of the GO layer at low temperatures (100^◦ C) avoids the drawbacks of chemical reduction methods such as toxicity and electrode metal damage during fabrication, while allowing for fine-tuning of the MR’s switching behavior. The device has analog switching characteristics, with a range of different resistance states. By taking advantage of the slow nature of GO thermal annealing, the switching properties of the rGO memristors can be precisely controlled by adjusting the reduction period. At short annealing times (i.e., T < 20 h), the devices switch from high to low resistance states, while at longer annealing times the switching behavior is reversed, with the device switching from low to high resistance states (LRS to HRS). Resistive switching occurs as a result of the diffusion and removal of the oxygen functional groups in the GO film caused by Joule heating induced by the electric current. Complete electrical characterization tests are presented along with wettability and X-ray diffraction (XRD) tests. This work opens a new vision for realizing rGO-based MR devices with tunable switching properties, broadening the application horizon of the device.

In this work we present SCRAMBLE, a configurable neuromorphic architecture that provides security against different threats by employing memristors for critical parts and functions. More specifically, we employ memristive memory cells - that are 3D stacked on top of the configurable neuromorphic hardware - to securely hold the weights as well as activation functions of any model processed on the generalized architecture. Thus, programmable memristive cells enable reconfiguration of the architecture to thwart both model stealing and hardware IP stealing attacks. We implement a proof-of-concept for the proposed architecture and analyze its security metrics. We also benchmark it against selected prior art for neuromorphic architectures to quantify the security-performance trade-offs.

The adverse effect of ultraviolet (UV) radiation on human beings has sparked intense interest in the development of new sensors to effectively monitor UV and solar exposure. This paper describes a novel low-cost and flexible graphene oxide (GO)-based paper sensor capable of detecting the total amount of UV or sun energy delivered per unit area. GO is incorporated into the structure of standard printing paper, cellulose, via a low-cost fabrication technique. The effect of UV and solar radiation exposure on the GO paper-based sensor is investigated using a simple color change analysis. As a result, users can easily determine the amount of ultraviolet or solar energy received by the sensor using a simple color analysis application. A neural network (ANN) model is also explored to learn the relation between UV color intensity and exposure time, then digitally display the results. The accuracy for the developed ANN reached 96.83%. The disposable, cost-effective, simple, biodegradable, safe, and flexible characteristics of the paper-based UV sensor make it an attractive candidate for a variety of sensing applications. This work provides new vision toward developing highly efficient and fully disposable GO-based photosensors. Graphical Abstract: [Figure not available: see fulltext.]