Behavior Of Devices Research Articles

Influence of stop and gate voltage on resistive switching of 1T1R HfO2-based memristors, a modeling and variability analysis

Memristive devices, particularly resistive random access memory (RRAM) cells based on hafnium oxide (HfO₂) dielectrics, exhibit promising characteristics for a wide range of applications. In spite of their potential, issues related to intrinsic variability and the need for precise simulation tools and modeling methods remain a medium-term hurdle. This study addresses these challenges by investigating the resistive switching (RS) behavior of different 1T1R HfO₂-based memristive devices under various experimental conditions. Through a comprehensive experimental analysis, we extract RS parameters using different numerical techniques to understand the cycle-to-cycle (C2C) and device-to-device (D2D) variability. Additionally, we employ advanced simulation methodologies, including circuit breaker-based 3D simulation, to shed light on our experimental findings and provide a theoretical framework to disentangle the switching phenomena. Our results offer valuable insights into the RS mechanisms and variability, contributing to the improvement of robust parameter extraction methods, which are essential for the industrial application of memristive devices.

Mind Over Matter: Effects of Digital Devices and Internet Dependence Perceptions and Behavior on Life Satisfaction in Singapore

Mind Over Matter: Effects of Digital Devices and Internet Dependence Perceptions and Behavior on Life Satisfaction in Singapore

Impact of Oxidation Layer in the Resistive Switching Behavior of Nitride-Based Memristor Devices

Impact of Oxidation Layer in the Resistive Switching Behavior of Nitride-Based Memristor Devices

A 2D Simulation‐Assisted Investigation of a Low Breakdown Voltage Module

Upscaling perovskites modules from laboratory to market specifications requires modules to resist heat, humidity, and partial shading. When a device is partially covered or damaged, the unbalanced photocurrent generated can create an irreversible damage. Mitigation via bypass diodes can be expensive. This might not be necessary for perovskite‐based modules. After an initial validation of the finite difference model for a simulation‐assisted investigation, a module is systematically damaged and characterized to reproduce an unbalanced photocurrent generation. The device shows a breakdown voltage (< 1 V), which is unexpectedly much lower than for a standalone perovskite cell (≈7 V). The effect is reproducible over time, and it demonstrates that in a reverse bias scenario, electricity can tunnel through the monolithic interconnection (P1P2P3) and operate similar to a low‐voltage bypass diode. Eventually, the current and voltage distribution via electroluminescence is mapped to validate the 2D simulations and reproduce the device behavior at different loads.

QS-Trust: An IoT ecosystem security model incorporating quality of service and social factors for trust assessment

In the rapidly growing and increasingly complex Internet of Things (IoT) ecosystem, securing communication and data exchanges between devices is a major concern. To address this, we proposed QS-Trust, a trust-based security model considering both Quality of Service (QoS) and social parameters. QS-Trust uses a trust value to determine the trust level between devices and employs a QoS-aware trust-based algorithm to improve the security of data transmissions. Additionally, the model incorporates intelligence parameters such as computing power, memory capacity, device behavior and context information to enhance the accuracy of trust evaluation. Our simulation results demonstrated that QS-Trust effectively improved the security of the IoT ecosystem while maintaining the high level of QoS. The execution time of QS-Trust was in the range of 21 to 128 milliseconds, which is efficient for real-time IoT applications. QS-Trust offers a promising solution for securing the IoT ecosystem. The QS-Trust model effectively addresses the challenges of maintaining accurate and up-to-date trust levels in dynamic IoT environments through its decentralized approach, multi-factor evaluations, and adaptive algorithms. By continuously monitoring device performance and interactions and dynamically adjusting trust scores, QS-Trust ensures that the IoT network remains secure and reliable.

Stable and Tunable Quantum Conductance in Spider-Silk-like Synaptic Device for Neurocomputing.

The quantum conductance (QC) behaviors in synaptic devices with stable and tunable conductance states are essential for high-density storage and brain-like neurocomputing (NC). In this work, inspired by the discontinuous transport of fluid in spider silk, a synaptic device composed of a silicon oxide nanowire network embedded with silicon quantum dots (Si-QDs@SiOx) is designed. The tunable QC behaviors are achieved in both the SET and RESET processes, and the QC states exhibit stable retention time exceeding 104 s in the synaptic device and show stable reproducibility after an interval of two months. The synaptic plasticity, including long-term potentiation/depression and Pavlovian conditioning function, is simulated based on the tunable conductance. The mechanism of stable and tunable QC behaviors is analyzed and clarified by beading effect of spider silk in Si-QDs@SiOx nanowires structure. The digit recognition capability of the device is evaluated by simulation using an artificial neural network consisting of the Si-QDs@SiOx-based synaptic device. These results provide insights into the development of neurocomputing systems with high classification accuracy.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene.

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.

High-Performance Broadband Self-Driven Photodetector Based on MoS2/Cs2CuBr4 Heterojunction.

Few-layer transition metal dichalcogenides and perovskites are both promising materials in high-performance optoelectronic devices. Here, we developed a self-driven photodetector by creating a heterojunction between few-layer MoS2 and lead-free perovskite Cs2CuBr4. The detector shows a unique property of very high sensitivity in a broad spectral range of 400 to 800 nm with response speed in a millisecond order. Current-voltage characteristics of the heterojunction device show rectifying behavior, in contrast to the ohmic behavior of the MoS2-based device. The rectifying behavior is attributed to the type II band alignment of the MoS2/Cs2CuBr4 heterojunction. The device shows a broadband (400 to 800 nm) photodetection with very high responsivity reaching up to 2.8 × 104 A/W and detectivity of 1.6 × 1011 Jones at a bias voltage of 3 V. The detector can also operate in self-bias mode with sufficient response. The photocurrent, photoresponsivity, detectivity, and external quantum efficiency of the device are found to be dependent on the illumination power density. The response time of the device is found to be ∼32 and ∼79 ms during the rise and fall of the photocurrent. The work proposes a MoS2/Cs2CuBr4 heterostructure to be a promising candidate for cost-effective, high-performance photodetector.

Characterizing parameter variations for enhanced performance and adaptability in 3 nm MBCFET technology

With the continuous scaling down of semiconductor devices, traditional transistor architectures face significant challenges in maintaining performance and power efficiency. Multi-bridge channel field-effect transistors (MBCFETs) are promising candidates for next-generation of transistors, enabling significant size reduction while preserving high performance. This paper investigates both n-type and p-type 3-nm MBCFETs with a focus on their behavior under diverse operating conditions. The study examines the influence of doping concentration, sheet thickness, temperature, device width, and the number of sheets, on the device's functionality aiming for high-performance applications. Doping concentrations of acceptors and donors ranging from 1 × 10 15 cm−3 - 9 × 10 17 cm−3 and 1 × 10 17 cm−3 - 9 × 10 19 cm−3, respectively, to observe their impact on device performance. Similarly, sheet thicknesses from 1 nm to 2 nm and device widths from 3 nm to 30 nm are analyzed to understand the scaling effects. The temperature varies from 273.15 K to 573.15 K to simulate different operational environments, while the number of sheets, ranging from 1 to 7, is adjusted to evaluate structural effects on device behavior. By extracting the subthreshold swing (SS), threshold voltage (Vth), ON-current (ION), OFF-current (IOFF), and the ION/IOFF ratio, the study offers valuable insights into the suitability and potential applications of N-MBCFET and P-MBCFET devices at the ultra-scaled 3 nm dimension. At room temperatures, the SS achieves 60 mV dec⁻1 for n-type and 64 mV dec⁻1 for p-type which indicates proper switching speed, with corresponding ON/OFF ratios of 2 × 106 and 1 × 105 for n-type and p-type, respectively. This study makes notable contributions to the field of nanoscale transistor technology, aiding in the design and optimization of future electronic devices.

Impact of inert electrode on the volatility and non-volatility switching behavior of SiO2-based conductive bridge random access memory devices

Impact of inert electrode on the volatility and non-volatility switching behavior of SiO2-based conductive bridge random access memory devices

Enhanced resistive switching behaviors of organic resistive random access memory devices by adding polyethyleneimine interlayer

Organic nonvolatile memory devices have garnered significant attention as next-generation electrical memory units owing to their potential for low-cost and straightforward fabrication through a solution process. In this study, we successfully fabricated fully solution-processed organic resistive random access memory (RRAM) devices using poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) as the resistive switching (RS) layer, with poly(3,4-ethylene-dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) employed as the top electrode. Additionally, to enhance performance further, a polyethyleneimine (PEIE) interlayer was introduced between the bottom electrode and the P3HT:PCBM RS layer. The resulting organic RRAM devices with the PEIE interlayer exhibited bipolar resistive switching, with improved endurance increasing from 50 to 100 cycles, a retention time of 103 s, and a low SET voltage of 0.7 V. The organic RRAM devices featuring the PEIE interlayer demonstrated superior RS performance attributed to the higher Schottky barrier at the interface between the bottom electrode and the active switching layer, creating an asymmetric structure. I–V curve fitting confirmed that the potential switching mechanism involved Schottky emission in the high-resistance state and Ohmic conduction in the low-resistance state. Our findings suggest that organic RRAM devices with a PEIE interlayer hold promise for stable nonvolatile memory applications.

Advancing IoT security with flame: A hybrid approach combining fuzzy logic and artificial lizard search optimization

The increasing usage of Internet of Things (IoT) devices has created a need for secure and efficient solutions to protect sensitive data from unauthorized access. However, the complicated and massive structure of IoT systems poses various security risks and challenges, especially in dynamic scenarios with high signaling overhead caused by subscriber mobility. So, in this paper, a Fuzzy-based Lightweight Authentication and Management of Encryption approach called ‘FLAME’ is proposed to solve the decentralized lightweight group key management problem by measuring the degree of security using fuzzy logic (FL) based on various factors like device and user behavior, network conditions, and resource availability. For effective key-based authentication, adopted an Artificial Lizard Search Optimization (ALSO) based RSA (Rivest, Shamir, Adleman) algorithm that generates private and public keys based on security evaluation outcome. The publishers and subscribers obtain encryption keys from the group key manager based on their security level, and dissemination is optimized by the ALSO algorithm. By leveraging the FL and ALSO based RSA algorithm, the system offers secure communication with limited utilization and protects confidential data in IoT environments. According to the analysis, results signify that the FLAME approach has a faster key generation, dissemination, and revocation time compared to existing approaches, along with reduced overhead during key management operations, and increased attack detection capacity of 98.7 %.

Enhancing IoT Security with Activity-Based Attack Modeling and Hybrid Classification Techniques

The proliferation of Internet of Things (IoT) devices in industrial environments (Industrial IoT or IIoT) has brought about significant advancements in automation and data analytics. However, the integration of these devices also introduces new security vulnerabilities, making them prime targets for cyber-attacks. This study aims to enhance the security of IIoT systems by employing an activity-based attack modeling approach coupled with hybrid classification techniques. Our proposed method leverages a hybrid GRU-LSTM model to detect and mitigate security threats in real-time. Activity-based attack modeling involves the analysis of device behavior and the identification of deviations from normal activity patterns. By focusing on the contextual behavior of IIoT devices, we can more accurately detect anomalies indicative of potential security breaches. The hybrid GRU-LSTM model, which combines the strengths of Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, is utilized to process the sequential data generated by IIoT devices. This combination enhances the model's ability to capture both short-term and long-term dependencies in the data, improving the detection accuracy of complex attack patterns. Our experimental results demonstrate that the proposed hybrid GRU-LSTM model achieves an impressive accuracy rate of 98.18% in identifying various types of cyber-attacks on IIoT systems. The implementation of this model at the edge of IIoT networks ensures real-time threat detection and response, minimizing the latency and reducing the dependency on centralized cloud computing resources. In conclusion, this research presents a robust and efficient approach to enhancing IIoT security through the integration of activity-based attack modeling and advanced hybrid classification techniques. The high accuracy of the proposed method highlights its potential for widespread adoption in securing IIoT environments against evolving cyber threats. This work contributes to the growing body of knowledge in IoT security and paves the way for further innovations in protecting industrial systems from cyber-attacks.

Interface trap-induced radiofrequency and low-frequency noise analysis under temperature variation of a heterostacked source L-gate tunnel field effect transistor

This work offers a comprehensive analysis of the adverse impact of interface trap charge (ITC) under the influence of temperature variation on a heterostacked (HS) source L-gate tunnel field effect transistor (TFET) having a SiGe pocket. An investigation of both static and radiofrequency (RF) characteristics has been carried out. It appears that ITCs situated at the Si–oxide interface fluctuate the flat-band voltage to alter the various analog/RF parameter characteristics. Uniform ITCs are seen to be less susceptible to degradation in device characteristics. Low-frequency noise (LFN) analysis has also been carried out considering the impact of different trap distributions (uniform and Gaussian) and densities, which are compared. The temperature dependence of LFN has been studied under the influence of different distributed ITCs, and this has rarely been explored. Moreover, a comparative analysis has been made of the device behavior and LFN characteristics of HS L-gate TFET structures with and without a SiGe pocket. Structures with SiGe pockets were found not to be susceptible to noise effects.

Automated Scanning Dielectric Microscopy Toolbox for Operando Nanoscale Electrical Characterization of Electrolyte‐Gated Organic Transistors

AbstractElectrolyte‐gated organic transistors (EGOTs) leveraging organic semiconductors' electronic and ionic transport characteristics are the key enablers for many biosensing and bioelectronic applications that can selectively sense, record, and monitor different biological and biochemical processes at the nanoscale and translate them into macroscopic electrical signals. Understanding such transduction mechanisms requires multiscale characterization tools to comprehensively probe local electrical properties and link them with device behavior across various bias points. Here, an automated scanning dielectric microscopy toolbox is demonstrated that performs operando in‐liquid scanning dielectric microscopy measurements on functional EGOTs and carries out extensive data analysis to unravel the evolution of local electrical properties in minute detail. This paper emphasizes critical experimental considerations permitting standardized, accurate, and reproducible data acquisition. The developed approach is validated with EGOTs based on blends of organic small molecule semiconductor and insulating polymer that work as accumulation‐mode field‐effect transistors. Furthermore, the degradation of local electrical characteristics at high gate voltages is probed, which is apparently driven by the destruction of local crystalline order due to undesirable electrochemical swelling of the organic semiconducting material near the source electrode edge. The developed approach paves the way for systematic probing of EGOT‐based technologies for targeted optimization and fundamental understanding.

Study of Process Variation in Nanotube Tunnel Field Effect Transistor

In the nanoscale, the process parameters and device dimension variation extensively affect the electrical performance of the device. Therefore, an inclusive study for the prediction of the overall device behavior is extremely necessary. In this manuscript, process variations caused by random dopant fluctuation (RDFs), variation of oxide thickness, and workfunction during fabrication are analyzed in junctionless nanotube TFET. The work quantitatively evaluates the impact of process variability on the various electrical parameters like energy band diagram, electric field, carrier concentration, and drain current of the nanotube TFET structure. The device simulation has been carried out with a 3-D SILVACO ATLAS simulator.

Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery.

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.

Facile synthesis of iodine variants-incorporated lithium perrhenate crystal and film: Resistive switching device and synaptic behavior

This study investigates the integration of iodine as a dopant into lithium perrhenate (LiReO4) using rhenium oxide (Re2O7) as a precursor, aiming to enhance the electrical conductivity of the compound, with implications for various material science applications. The iodine variants-incorporated LiReO4 (Iv@LiReO4·H2O) exhibits a notable improvement in the average electrical conductivity (∼ 2 S/cm) and light absorption across a broad spectrum, extending to the near-infrared range. Additionally, the doping process induces resistive switching and synaptic behaviors due to iodine diffusion. The presence of iodine and the partial reduction of Re oxide is confirmed by X-ray photoelectron spectroscopy, while UV-Vis absorption spectroscopy indicates the existence of incorporated iodine variants species. Density functional theory calculations further elucidate the mechanism by which iodine doping triggers a significant enhancement in electrical conductance, primarily by introducing a mixed-valence oxidation state in rhenium, leading to the narrowing of the band gap from 3.68 eV to 0.33 eV. This research suggests potential uses for Iv@LiReO4·H2O in applications to memristive neuromorphic devices.

Advances in Microfluidic Systems and Numerical Modeling in Biomedical Applications: A Review.

The evolution in the biomedical engineering field boosts innovative technologies, with microfluidic systems standing out as transformative tools in disease diagnosis, treatment, and monitoring. Numerical simulation has emerged as a tool of increasing importance for better understanding and predicting fluid-flow behavior in microscale devices. This review explores fabrication techniques and common materials of microfluidic devices, focusing on soft lithography and additive manufacturing. Microfluidic systems applications, including nucleic acid amplification and protein synthesis, as well as point-of-care diagnostics, DNA analysis, cell cultures, and organ-on-a-chip models (e.g., lung-, brain-, liver-, and tumor-on-a-chip), are discussed. Recent studies have applied computational tools such as ANSYS Fluent 2024 software to numerically simulate the flow behavior. Outside of the study cases, this work reports fundamental aspects of microfluidic simulations, including fluid flow, mass transport, mixing, and diffusion, and highlights the emergent field of organ-on-a-chip simulations. Additionally, it takes into account the application of geometries to improve the mixing of samples, as well as surface wettability modification. In conclusion, the present review summarizes the most relevant contributions of microfluidic systems and their numerical modeling to biomedical engineering.

A Lego-Like Reconfigurable Microfluidic Stabilizer System with Tunable Fluidic RC Constants and Stabilization Ratios.

In microfluidic systems, it is important to maintain flow stability to execute various functions, such as chemical reactions, cell transportation, and liquid injection. However, traditional flow sources, often bulky and prone to unpredictable fluctuations, limit the portability and broader application of these systems. Existing fluidic stabilizers, typically designed for specific flow sources, lack reconfigurability and adaptability in terms of the stabilization ratios. To address these limitations, a modular and standardized stabilizer system with tunable stabilization ratios is required. In this work, we present a Lego-like modular microfluidic stabilizer system, which is fabricated using 3D printing and offers multi-level stabilization combinations and customizable stabilization ratios through the control of fluidic RC constants, making it adaptable to various microfluidic systems. A simplified three-element circuit model is used to characterize the system by straightforwardly extracting the RC constant without intricate calculations of the fluidic resistance and capacitance. By utilizing a simplified three-element model, the stabilizer yields two well-fitted operational curves, demonstrating an R-square of 0.95, and provides an optimal stabilization ratio below 1%. To evaluate the system's effectiveness, unstable input flow at different working frequencies is stabilized, and droplet generation experiments are conducted and discussed. The results show that the microfluidic stabilizer system significantly reduces flow fluctuations and enhances droplet uniformity. This system provides a new avenue for microfluidic stabilization with a tunable stabilization ratio, and its plug-and-play design can be effectively applied across diverse applications to finely tune fluid flow behaviors in microfluidic devices.