Integration Of Devices Research Articles

On-Chip Thermoelectric Devices Based on Standard Silicon Processing.

The strong reduction of thermal conductivity with respect to bulk silicon makes nanostructured silicon one of the best materials for highly efficient direct conversion of heat into electrical power and vice-versa. The widespread technologies for the integration of silicon devices can be used to define on-chip micro thermoelectric generators (scavengers); similar structures could also be used for precise and well-localized cooling through the reverse process of heat pumping. However, the road to the fabrication of integrated thermal energy scavengers or cooler, based on silicon, is still very long. In this work, the design and the fabrication process of on-chip thermoelectric devices based on a large number of interconnected monocrystalline silicon nanobeams, very tall (>1 µm) and thin (less than 200 nanometers), arranged in large areas combs is shown. The small width of the nanobeams gives a reduced thermal conductivity, and the height perpendicular to the substrate allows the definition of a highly dense collection of nanostructures. The total cross-sectionis far broader than that of other nanostructures, a characteristic that guarantees both mechanical stability and larger deliverable power per unit area.

Integrating IoT, Machine Learning, and Blockchain for Enhanced Network Security and Efficiency: A Review

The rapid expansion of the Internet of Things (IoT) has led to significant improvements in automation, communication, and data exchange across various industries. However, the integration of numerous interconnected devices has also heightened concerns regarding network security, data privacy, and system efficiency. This review explores the intersection of IoT, machine learning (ML), and blockchain technology as a holistic approach to enhancing network security and operational efficiency. IoT devices generate vast amounts of data, which, when combined with machine learning algorithms, can improve predictive analytics, anomaly detection, and real-time decision-making. Blockchain technology further strengthens this ecosystem by providing a decentralized and immutable ledger, ensuring secure data transmission and reducing vulnerabilities. The paper discusses recent advancements, key challenges, and future research directions, highlighting the potential of combining IoT, ML, and blockchain to create robust, secure, and efficient networks. Specific use cases, such as in healthcare, supply chain management, and smart cities, are also examined to illustrate practical implementations. This review concludes with insights into the future potential of these technologies and the challenges that remain in their integration.

Fabrication of bacteriorhodopsin-embedded hydrogel construct for biocompatible photosensitive device

Bacteriorhodopsin (br) is a promising photosensitive material with applications in energy conversion, biosensors, and optoelectronic devices due to its bio-sourced origin and photoelectrical properties. Despite advancements in recent years, the integration of br-based devices necessitates the use of materials with little biocompatibility and fabrication techniques with restricted customizability, limiting potential applications such as optocontrol of cell behavior, implants with 3D patterning for retinal disease treatment, and light-sensitive cell robots. To solve this limitation, this study presents a novel approach by embedding br into a hydrogel matrix. It utilizes an extrusion-based and 3D bioprinting technique, showcasing its light-sensitive characteristics within a fully biocompatible construct. The hydrogel, comprising gelatin and sodium alginate, offers excellent printability for generating structured designs with versatile patterns. Photoelectrical properties of the fabricated br-embedded hydrogel construct, such as differential response, light intensity sensitivity, and br concentration sensitivity, are identified through electrochemical characterization. The temporal and spatial pattern recognition ability, based on the photoelectrical characteristics, is demonstrated through modulated light illumination and different patterns of printed hydrogel construct. Pattern recognition ability was then applied to reconstruct images containing different Latin letters. This research presents a novel method for the fabrication of patterned hydrogel constructs with high biocompatibility and distinctive light-responsive properties, expanding the potential applications of br in bio-related scenarios.  

Multistacked Graphene Grown by Copper-Pocket-Assisted Chemical Vapor Deposition for Versatile Device Integration

Multistacked Graphene Grown by Copper-Pocket-Assisted Chemical Vapor Deposition for Versatile Device Integration

Highly sensitive room-temperature ammonia sensor based on PbS quantum dots modified SnS2 nanosheets and theoretical investigation on its sensing mechanism by DFT calculation

Real-time accurate detection of hazardous gases is an urgent issue for industrial development and safeguarding public health. However, large-scale application of gas sensors in the network requires low-power consumption of sensing device for successful integration in smart platform. Although traditional ammonia (NH3) sensors based on semiconductor materials possess various advantages, it still remains the challenge in high operating temperature. Herein, PbS quantum dots (QDs) with higher adsorption energy were used to sensitize SnS2 nanosheets with electron transport capacity to achieve excellent NH3 sensing performance. PbS QDs with an approximately diameter of 10 nm were dispersed on SnS2 nanosheets with an average thickness of 14 nm. Impressively, the optimal sensor employing PbS QDs/SnS2 nanocomposites exhibited high sensor response of 8.5 to 100 ppm NH3, good response/recovery times of 9 s/720 s, as well as excellent selectivity, repeatability, and long-term stability at room temperature of 25 °C. The adsorption behavior and electronic structure of PbS QDs/SnS2 nanocomposites before and after NH3 adsorption were investigated based on density functional theory calculation. Moreover, the sensing mechanism of PbS QDs/SnS2 nanocomposites was also discussed. The designed PbS QDs/SnS2 nanocomposites hold considerable promise as a candidate in realizing superior NH3 sensing performance at room temperature.

Effect of strain gradient and interface engineering on the high-temperature energy storage capacitors

The miniaturization and high integration of electronic devices pose new requirements for the energy storage density and high-temperature performance of dielectric capacitors. For thin film materials, internal stress and the interface layer often show a significant impact on their energy storage performance. Therefore, the capacitors with different stress gradient sequences and different periods were designed by BaHf0.17Ti0.83O3 (BHTO17), BaHf0.25Ti0.75O3 (BHTO25), and BaHf0.32Ti0.68O3 (BHTO32) to investigate the effect of stress gradient and interface engineering on the energy storage characteristics. Dielectric thin film structures with upward gradient, downward gradient, and periodic upward gradient (4N) were constructed. The study found that the upward gradient structure had higher breakdown field strength than the downward gradient structure. This is because the upward gradient structure can effectively extend the ending electric field of the Ohmic conduction mechanism and delay the activation electric field of the F–N tunneling mechanism. The 4N structure had a slightly higher breakdown field strength (reaching 9.22 MV/cm) compared to the pure upward gradient structure. The 4N structure thin film also exhibited higher energy storage density (115.44 J/cm3) and wide temperature (−100 to 400 °C) characteristics. These findings provide important guidance and application value for improving the energy storage characteristics of dielectric capacitors at high temperatures through structural design.

Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems

With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.

Lab-on-a-chip paper-based electrochemically assisted solid-phase microextraction and ion selective sensor for determination of naproxen in biological fluid samples

BackgroundThe integration of paper-based analytical devices with lab-on-a-chip technology has opened up new possibilities for miniaturized, rapid, low cost and portable diagnostic testing in resource limited settings. This work introduces an integrated lab-on-a-chip platform coupling paper-based analytical devices (LOC-PADs) for dual usage: electrochemically controlled solid-phase microextraction (EC-SPME) and determination of naproxen (NAP) by potentiometric ion-selective electrode (ISE) in biological samples. ResultsConductive papers were successfully fabricated by a chemical procedure. Thereupon, a conducting molecularly imprinted polymer (CMIP) film based on PPy and an anionic model drug (NAP) as a template were applied to the conductive paper substrate via electrochemical methods. A microfluidic chip device was employed to assess the analytical performance of the fabricated paper-based CMIP. The designed chip is divided into two parts with a microfluidic channel between them. The first chamber is an extraction zone for EC-SPME of NAP by using the CMIP paper as a selective sorbent. The second chamber is the detection zone and consists of a paper-based NAP ion selective sensor for potentiometric detection of drug. The analytical process was investigated and optimized for each chamber. Remarkable selectivity towards NAP was achieved in the concentration range from 4.0 × 10−7 to 1.0 × 10−2 mol L−1 with determination coefficient (R2 = 0.99) and the limit of detection (LOD) was 2.0 × 10−7 mol L−1. Anionic Nernstian compliance was gained −58.5 ± 0.5 mV decade−1, and response time in the detection step was 7s for ISE. Satisfactory spiking recovery values within the acceptable range of 90–110 % with RSDs ≤7 % were achieved for the analysis of real samples with fully portable LOC device. SignificanceThe LOC-PADs were favorably used for selective extraction and determination of NAP in serum and saliva samples, respectively. EC-SPME caused sample clean-up, reduced or eliminated different interferences and enhanced selective response of ISE as detection zone of device. Integration of EC-SPME and ISE in a single chip enabled easy and fast determination of trace amounts of NAP in limited real sample volumes, and fully portable advantages.

Photonic crystal topological interface state modulation for nonvolatile optical switching

Phase change materials (PCMs), characterized by high optical contrast (Δn>1), nonvolatility (zero static power consumption), and quick phase change speed (∼ns), provide new opportunities for building low-power and highly integrated photonic tunable devices. Optical integrated devices based on PCMs, such as optical switches and optical routers, have demonstrated significant advantages in terms of modulation energy consumption and integration. In this paper, we theoretically verify the solution for a highly integrated nonvolatile optical switch based on the modulation of the topological interface state (TIS) in the quasi-one-dimensional photonic crystal (quasi-1D PC). The TIS exciting wavelength changes with the crystalline level of the PCM. The extinction ratio (ER) of the topological optical switch is over 18 dB with a modulation length of 9 μm. Meanwhile, the insertion loss (IL) can be controlled within 2 dB. Furthermore, we have analyzed the impact of fabrication errors on the device’s performance. The obtained results show that, the topological optical switch, which changes its “on/off” state by modulating TIS, exhibits enhanced robustness to the fabrication process. We provide an interesting and highly integrated scheme for designing the on-chip nonvolatile optical switch. It offers great potential for designing highly integrated on-chip optical switch arrays and nonvolatile optical neural networks.

Periodic Domain Inversion in Single Crystal Barium Titanate-on-Insulator Thin Film.

Experimentally achieving the first-ever electric field periodic poling of single crystal barium titanate oxide(BTO, or BaTiO3) thin film on-insulator is reported. Owing to the outstanding optical nonlinearities of BTO, this result is a key step toward achieving quasi-phase-matching (QPM). First, the BTO thin film is grown on a dysprosium scandate substrate using pulsed laser deposition with a thin layer of strontium ruthenate later serving as the bottom electrode for poling. The characterization of the BTO thin film using x-ray diffraction (XRD) and piezo-response force microscopy to demonstrate single crystal, single domain growth of the film that enables the desired periodic poling, are presented. To investigate the poling quality, both non-destructive piezo force response microscopy and destructive etching-assisted scanning electron microscopy (SEM) are applied, and it is shown that high quality, uniform, and intransient poling with 50% duty cycle and periods ranging from 2µm to 10µm is achieved. The successful realization of periodic poling in BTO thin film unlocks the potential for highly efficient nonlinear processes under QPM that seemed far-fetched with prior polycrystalline BTO thin films which predominantly relied on efficiency-limited random or non-phase matching conditions and is a key step toward integration of BTO photonic devices.

Flexible Crossbar Molecular Devices with Patterned EGaIn Top Electrodes for Integrated All-Molecule-Circuit Implementation.

Here, a unique crossbar architecture is designed and fabricated, incorporating vertically integrated self-assembled monolayers in electronic devices. This architecture is used to showcase 100 individual vertical molecular junctions on a single chip with a high yield of working junctions and high device uniformity. The study introduces a transfer approach for patterned liquid-metal eutectic alloy of gallium and indium top electrodes, enabling the creation of fully flexible molecular devices with electrical functionalities. The devices exhibit excellent charge transport performance, sustain a high rectification ratio (>103), and stable endurance and retention properties, even when the devices are significantly bent. Furthermore, Boolean logic gates, including OR and AND gates, as well as half-wave and full-wave rectifying circuits, are successfully implemented. The unique design of the flexible molecular device represents a significant step in harnessing the potential of molecular devices for high-density integration and possible molecule-based computing.

Improving mobile security: A study on android malware detection using LOF

Abstract The ubiquity of smartphones in our daily lives has made them attractive targets for malicious actors seeking to compromise user data and device functionality. Android malware detection has become imperative to protect user privacy and device integrity. This paper presents a focused study on leveraging the Local Outlier Factor (LOF) method for Android malware detection using the DREBIN dataset. Our research addresses the need for accurate and efficient Android malware detection. We explore the LOF method, an anomaly-based detection technique, to assess its effectiveness in distinguishing malicious applications from benign ones within the Android ecosystem. Rigorous experiments using the extensive DREBIN dataset reveal LOF's superiority in accuracy, precision, recall, and False Positive Rate (FPR). We introduce additional metrics like Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and True Negative Rate (TNR) to comprehensively evaluate LOF. Our findings highlight LOF's ability to balance false positives and false negatives, making it an ideal choice for Android malware detection. We emphasize the importance of representative datasets, such as DREBIN, for validation. In conclusion, this research positions LOF as a reliable tool for Android malware detection, offering robust protection against emerging threats. As mobile technology evolves, our study encourages further exploration of advanced techniques and real-world deployment scenarios.

Enhancing Performance in Top‐Illuminated Shortwave Infrared Organic Photodetectors via Microcavity Resonance

AbstractShortwave infrared (SWIR) image sensors have unique functions in many optical applications, leading to widespread attention in developing next‐generation materials and photodetector technologies. Organic photodetectors (OPDs) are highly promising due to their flexibility in molecular design and processability. However, integrating OPDs with silicon readout integrated circuits (ROICs) poses numerous challenges, often resulting in underestimated device performance and limiting technological progress. To address the requirements of integrating top‐illuminated OPD with ROICs and to enhance the external quantum efficiency (EQE), optical microcavities are introduced into the OPDs. The EQE in the SWIR region can be effectively enhanced by properly adjusting the thicknesses of the photoactive layer (PAL) and interlayers. Simulations of the optical field distribution further support the active functions of the microcavity structure. The spatial variation of the microcavities allows the spectral response to shift from 1000 to 1400 nm, and the optimized device achieves an EQE of 25.8% at 1260 nm. Finally, the OPDs are integrated with a silicon‐based test kit, and the results reveal comparable sensing performance, demonstrating the high potential of microcavity resonance for device integration. This design effectively improves the integration of OPDs with traditional ROICs and advances SWIR‐based organic image sensor technology further toward commercialization.

Second-harmonic radiation by on-chip integrable mirror-symmetric nanodimers with sub-nanometric plasmonic gap

Abstract Second-harmonic generation (SHG) facilitated by plasmonic nanostructures has drawn considerable attention, owing to its efficient frequency up-conversion at the nanoscale and potential applications in on-chip integration and nanophotonic devices. Herein, we present a nanodimer array fabricated by nanoimprinting, composed of nanofinger-pair symmetrically leaning at an off-angle with a well-defined sub-nanometric gap. Commonly, geometric symmetry would suppress the far-field SHG due to the near-field cancelling of symmetric surface SH polarization. However, we find that the light-induced surface SH polarization distribution along the wave-vector of incidence could be influenced by the off-angle, which is consistent to the requirement of SH polarization symmetry-breaking in symmetric metallic nanocavity. A dramatic enhancement of far-field SHG is achieved by tuning the off-angle of nanofinger-pair, even approaching up to over 4 orders of magnitude for an optimal value. The demonstration of SHG enhancement on our well-defined plasmonic nanodimer provides a new way of on-chip integration to activate high-efficient SH radiation, which might be potential for applications in novel nonlinear optical nanodevices with remarkable efficiency and sensitivity.

Image Sensors and Photodetectors Based on Low‐Carbon Footprint Solution‐Processed Semiconductors

AbstractThis mini‐review explores the evolution of image sensors, essential electronic components increasingly integrated into daily life. Traditional manufacturing methods for image sensors and photodetectors, employing high carbon footprint techniques like thermal evaporation and chemical vapor deposition, are being replaced by environmentally conscious solution processing. Organic and Colloidal Quantum Dot‐based image sensors emerge as promising candidates, aligning with the shift toward solution‐based device integration. This review provides insights into the working principles of photodetectors and image sensors, summarizing relevant materials and fabrication approaches. Additionally, it delves into the detailed exploration of pixelated patterning techniques and their potential applications in the realm of solution‐processed image sensor fabrication.

Modulating intramolecular charge transfer via π-d conjugative effect in coordination polymers toward photo-thermo-electric conversion

The prospect of harnessing sunlight to generate cost-effective heat and electricity presents a captivating opportunity to address water scarcity and electricity shortages. Photothermal materials with a broad absorption spectrum are indispensable for effectively harnessing solar energy and converting it into heat/electricity. Herein, we developed an intramolecular charge transfer strategy via conjugated effect to regulate solar absorption and photothermal conversion ability of M-DABDT (M = Fe/Co/Ni/Cu/Zn, DABDT = 2,5-diaminobenzene-1,4-dithiol), overcoming a common limitation of narrow absorption and low photothermal efficiency in traditional metal-organic assemblies. The density functional theory calculations reveal that altering transition metal ions induces the structural torsion of organic linkers, resulting in a significant change in dihedral angle from 89.96° to 2.57°. The structural change significantly facilitates the charge transfer and electron relaxation, ultimately promoting the conversion of light to heat. Under one sun irradiation, the maximum temperature of Fe-DABDT can reach approximately 92.5 ℃. More significantly, the integration of M-DABDT CPs and thermoelectric devices under normal solar irradiation results in an extraordinary open circuit voltage (238 mV) and maximum power density (364.5 μW m−2), processing a peak output voltage density of 76.9 V m−2 at 11:00 a.m. in the presence of outdoor sunlight. This strategy presents an avenue for developing metal-organic solar absorbers for photo-thermo-electric conversion systems.

Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles

The integration of external devices and network connectivity into autonomous vehicles has raised significant concerns about in-vehicle security vulnerabilities. Existing security mechanisms for in-vehicle bus systems, which mainly rely on appending authentication codes and data encryption, have been extensively studied in the context of CAN and CAN-FD buses. However, these approaches are not directly applicable to Ethernet buses due to the much higher data transmission rates of Ethernet buses compared to other buses. The real-time encryption and decryption required by Ethernet buses cannot be achieved with conventional methods, necessitating an acceleration in the speed of cryptographic operations to match the demands of Ethernet communication. In response to these challenges, our paper introduces a range of cryptographic solutions specifically designed for in-vehicle Ethernet networks. We employ an AES-ECC hybrid algorithm for critical vehicle control signals, combining the efficiency of AES with the security of ECC. For multimedia signals, we propose an improved AES-128 (IAES-128) and an improved MD5 (IMD), which improve encryption time by 15.77%. Our proposed security mechanisms have been rigorously tested through attack simulations on the CANoe (version 10) platform. These tests cover both in-vehicle control signals, such as braking and throttle control, and non-critical systems like multimedia entertainment. The experimental results convincingly demonstrate that our optimized algorithms and security mechanisms ensure the secure and reliable operation of real-time communication in autonomous vehicles.

Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration

With the development of artificial intelligence (AI) and high-performance computing (HPC), the microelectronic industry is challenged with increased device integration density. Chiplet architecture can break through a variety of limitations on the system-on-chip (SoC) to create a high-computility system. However, chiplet heterogeneous integration suffers from high heat flux and serious thermal interaction problems. The factors affecting thermal interaction are not clear. In this paper, a collective parameter model and a distribution parameter model are developed to clarify the optimization method to mitigate thermal interaction. The trends predicted by the parameter model are consistent with the finite element method (FEM) simulation results. Furthermore, to cool the chiplets, a thermal test vehicle is designed and fabricated, and the cooling performance of the test vehicle with different flow rates, different TIMs (Thermal Interfacial Materials) (DOW5888 vs. liquid metal), and different heat modes is experimentally investigated. Compared with DOW5888, the utilization of liquid metal TIM can mitigate thermal interaction by 56% and 76% at flow rates of 0.2 L/min and 0.8 L/min, respectively. Consequently, at a temperature rise of 60 °C and a flow rate of 0.8 L/min, using liquid metal TIM can achieve heat fluxes of 330 W/cm2 and 520 W/cm2 for two chiplets, respectively.

Fluorinated boron nitride nanosheets for high thermal conductivity and low dielectric constant silicone rubber composites

AbstractIncreasing miniaturization and integration of microelectronic devices have led to an unprecedented attention on heat dissipation and signal transmission of electronic equipment. However, the mostly used method to enhance the thermal conductivity of polymers by adding thermally conductive fillers usually results in the increase of dielectric constant (Dk). It was still challenging to synergistically achieve low Dk and high thermal conductivity of polymer‐matrix composites. Herein, hydroxylated boron nitride nanosheet (BNNS‐OH) with a high yield of 35.67% was prepared by the liquid ultrasonic exfoliation under the assistance of sodium cholate (SC), and grafted with (1H,1H,2H,2H‐perfluorodecyl) trimethoxy silane to prepare fluorinated boron nitride nanosheets (F‐BNNS), which can uniformly disperse in liquid silicone rubber (LSR) and reduce the Dk of LSR composites. The thermal conductivity of LSR composites with 20 wt% F‐BNNS reached 0.489 W·m−1·K−1, showing an increase of 307.35% in comparison with pure LSR (0.12 W·m−1·K−1). Meantime, the Dk of as‐obtained LSR/F‐BNNS (20 wt%) composites is reduced from 3.4 to 2.6, and the dielectric loss (Df) is less than 0.012. The facile and rational design of fluorinated BNNS offers a new insight for the high‐end electronic packaging materials.Highlights A new method of exfoliation and fluorinated treatment of h‐BN was proposed. The LSR composites achieved an ultralow Dk of 2.63 via fluorinated BNNS. Thermal conductivity of LSR composites increased by 307.35% than pure LSR.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.