The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for ultra-low power and energy-efficient VLSI circuit designs. One of the major challenges in nanoscale CMOS technology is the significant increase in leakage power, which adversely affects battery life and system reliability in always-on IoT applications. This paper presents a low-leakage CMOS design using Dynamic Threshold Scaling (DTS) to effectively minimize static power dissipation while maintaining high performance. The proposed approach dynamically adjusts the threshold voltage (Vth_{th}th) of MOS transistors based on the operating conditions, enabling a trade-off between leakage reduction and switching speed. In the proposed architecture, adaptive body biasing and voltage control mechanisms are integrated to modulate the threshold voltage in real time. A 45 nm CMOS technology node is considered for implementation and simulation. Experimental results demonstrate that the proposed DTS-based CMOS design achieves a leakage power reduction of approximately 38.7%, along with a 22.5% improvement in energy efficiency compared to conventional fixed-threshold CMOS circuits. Additionally, the design maintains a delay variation within 8%, ensuring reliable performance for IoT workloads. The proposed method is particularly suitable for battery-operated and energy-constrained IoT devices such as wearable sensors, smart home systems, and remote monitoring units. Overall, this work provides a scalable and efficient solution for next-geerationn low-power CMOS circuit design in IoT ecosystems.
The rapid growth of energy-constrained applications such as Internet of Things (IoT), wearable electronics, and biomedical implants has intensified the need for ultra-low power circuit design methodologies. Subthreshold computing has emerged as a promising solution by operating transistors below the threshold voltage, significantly reducing dynamic power consumption. However, leakage currents become dominant in this regime, leading to degraded performance and reliability challenges. This paper presents a leakage-aware FinFET-based CMOS design framework optimized for sub-threshold computing applications. The proposed approach leverages the superior electrostatic control and reduced short-channel effects of FinFET technology to mitigate leakage currents while maintaining acceptable performance levels. A combination of adaptive threshold control, multi-fin optimization, and leakage reduction techniques such as gate work-function engineering and power gating is incorporated into the design. The proposed architecture is evaluated using standard benchmark circuits, demonstrating a significant reduction in leakage power by up to 35-45% compared to conventional planar CMOS designs, while maintaining competitive delay characteristics. Furthermore, the design shows improved energy efficiency and robustness against process variations, making it suitable for next-generation low-power embedded systems. The results highlight that FinFET-based CMOS circuits provide a viable pathway for achieving energy-efficient and reliable sub-threshold operation in modern nano-scale technologies.
This paper designs a W-band compressive imaging system using a flat metamaterial aperture to perform high-resolution imaging without mechanical scanning or costly phased arrays. The proposed system employs a 2D metamaterial-based parallel plate waveguide integrated with cELC elements, excited by frequency-diverse radiation patterns. By leveraging compressive sensing principles, the system reconstructs sparse scenes through a measurement model, which is considered as a linear inverse problem and regularized using prior sparsity constraints. Key metrics, including SVD spectra, MSE, and effective sparsity, are analyzed to optimize crucial parameters such as aperture density, bandwidth, element quality factor, and spatial distribution. Trade-off studies demonstrate quasi-linear performance dependence on frequency points and bandwidth, along with minimal sensitivity to element density. An experimental validation is conducted using a synthesized aperture with 43 receiver positions and a single transceiver. The obtained results demonstrates high consistency between the simulations and measured results, though target specularity limits the full resolution. Furthermore, effective sparsity is considered as a unified metric for system evaluation, demonstrating the feasibility of W-band metamaterial-based compressive imaging. This approach provides a cost-effective solution for millimeter-scale resolution in security, aerospace, ad industrial applications, allowing to balance nthe performance and practical implementation constraints.
Heat generated during charging and discharging cycles can degrade lithium-ion battery performance and lead to thermal runaway. This study presents an immersion liquid cooling system for prismatic lithium-ion energy storage battery modules, and evaluates its thermal performance through experiments and simulations. Experimental results indicated that natural air cooling allowed both the average module temperature and maximum temperature difference to exceed safe limits. Immersion cooling reduced the maximum temperature difference by 33.1% and the average temperature by 17.3% compared to natural cooling. Optimal thermal performance occurred at a coolant temperature of 25 degrees C and a flow rate of 1.75 L/min; further increases in flow rate or temperature reduced efficiency. To explore the influence of system design, numerical simulations using ANSYS Fluent integrated fluid-thermal coupling model and Battery heat generation model. This study investigated the effects of inter-battery spacing, tank dimensions, and novel structural designs. An 8 mm spacing provided the best compromise between flow behavior and heat transfer characteristics. Structural optimization of the immersion tank was found to reduce the maximum temperature difference across the module by up to 14.36%. An ejector-style inlet-outlet configuration combined with a layered design reduced the average temperature by 2.17 degrees C and decreased the maximum temperature difference by 36.34% compared with the initial case. These findings demonstrate that the optimized structural design can remarkably improve cooling performance, thereby offering a practical and viable solution for the immersion liquid cooling system of lithium-ion batteries.
Ruthenium (Ru) has emerged as a critical material for nanoscale interconnects in advanced integrated circuits due to its short electron mean free path (6.59 nm), high melting point (similar to 2334 degrees C), and superior electromigration resistance. As technology nodes scale toward 2 nm and below, conventional copper interconnects suffer from severe resistivity increases caused by size-dependent electron scattering. This review systematically examines the application progress of Ru in nanoscale interconnect technologies, including buried power rails, backside power distribution networks, and high-aspect-ratio via structures. Unlike copper, Ru eliminates the need for thick diffusion barrier layers such as Ta/TaN, reducing overall resistance and enabling direct integration with low-k dielectrics. Recent breakthroughs in grain boundary engineering, including atomic layer deposition combined with high-temperature annealing, have achieved Ru grain sizes exceeding 100 nm, reducing resistivity to near its theoretical limit (7.6 & micro;.s2 & centerdot; cm). Furthermore, Ru-based diffusion barriers (e.g., RuTiN, Ru-Mo-C, Ru-Mn) and seed layers have demonstrated exceptional performance in suppressing Cu diffusion and enhancing interfacial adhesion. Emerging applications in direct metal etching, airgap integration, self-aligned vias, and two-dimensional material heterojunctions (graphene, MoS2) are also discussed. Finally, this review summarizes current challenges in high-purity Ru preparation, compares chemical and physical synthesis routes, and outlines future directions for metastable phase engineering and low-dimensional Ru structures. The findings position Ru as a transformative material for sustaining nanoelectronic device scaling beyond the 5 nm node.
In the control system of permanent magnet synchronous motor (PMSM) driven by inverter, the traditional Space Vector Pulse Width Modulation (SVPWM) technology will produce high frequency current harmonics at the switching frequency and frequency doubling of inverter, the high frequency electromagnetic vibration of PMSM will be produced in the process of operation. Therefore, a double random SVPWM control method combining random switching frequency and random zero vector is proposed, which is applied to the harmonic spectrum expansion of high frequency current of permanent magnet synchronous motor, the method can disperse the high frequency harmonics concentrated in the switching frequency and its double frequency, and effectively weaken the high frequency electromagnetic vibration.
This paper presents the design and analysis of a nanoelectronics-enabled semi-circled horn-inspired bow-tie Multiple-Input Multiple-Output (MIMO) antenna with superior port isolation for next-generation wireless applications. The proposed antenna integrates nanoelectronic components, such as nanoscale conductive layers and compact decoupling elements, to enhance electromagnetic field confinement and minimize mutual coupling between closely spaced radiating elements. The semi-circled horn-like geometry is optimized to achieve wide impedance bandwidth and directional radiation characteristics, while the bow-tie structure ensures compactness and multi-band operability. A novel nanoelectronic-assisted isolation mechanism is introduced, which significantly suppresses surface current propagation across antenna ports. The antenna operates in the 3.2-10.5 GHz frequency range, covering sub-6 GHz and ultra-wideband (UWB) applications. Simulation and experimental results demonstrate an impedance bandwidth exceeding 120%, with a reflection coefficient (|S11|) below-10 dB. The proposed MIMO configuration achieves a high port isolation of better than-28 dB, envelope correlation coefficient (EC) less than 0.01, ad diversity gain close to 9.98 dB, indicating excel-Cn lent diversity performance. Furthermore, the integration of nanoelectronic materials contributes to improved radiation efficiency of approximately 92% and gain enhancement up to 7.5 dBi. The compact size, enhanced isolation, and high efficiency make the proposed antenna a strong candidate for 5G, 6G, IoT, and high-speed wireless communication systems.
The sustainable recovery of spent lead paste (SLP) from lead-acid batteries has traditionally focused on metallurgical lead regeneration, often overlooking its potential for high-value optoelectronic and nanoelectronic materials. This review re-evaluates SLP recycling through the lens of nanotechnology and device engineering. We systematically analyze pretreatment strategies (liquid-phase leaching and solid-to-solid conversion) and subsequent synthesis pathways that yield not only metallic lead and battery-grade compounds but also advanced functional materials such as lead halide perovskites, PbS quantum dots, lead-based aerogels, and piezoelectric ceramics. Emphasis is placed on how processing parameters, such as precursor purity, calcination atmosphere, and crystallization conditions, influence the nanoscale morphology, crystal phase, and optoelectronic performance of recycled products. Key findings include the successful fabrication of perovskite solar cells (PCE up to 20.45%), PbS quantum dot photodetectors (EQE 49.6%), and Pb(Zr, Ti)O 3 piezoelectrics (d 33 ~270 pC N −1 ) from SLP-derived precursors. By bridging waste recycling and functional nanomaterials, this review provides a roadmap for integrating secondary lead resources into the circular economy of nanoelectronics and optoelectronics, addressing both environmental sustainability and material innovation.
Poultry farming is highly vulnerable to infectious diseases such as Newcastle disease, avian influenza, and coccidiosis, which cause severe economic losses if not detected early. Conventional diagnostic methods are slow, require skilled personnel, and are not scalable for large farms. This paper proposes a real-time poultry health monitoring framework using CMOS VLSI biosensors integrated with on-chip hardware accelerators for acoustic, biochemical, and visual disease detection. A CMOS photodiode array was designed to capture biochemical fluorescence markers with a sensitivity of 92.4% at 10 & micro;M antigen concentration. An NMOS amplifier + band-pass filter front-end processed cough/cluck audio signal, achieving a signal-to-noise ratio (SNR) improvement of 18.7 db. For classification, a hardware CNN-GRU accelerator fabricated in 65 nm CMOS was implemented, consuming 28.6 mW power at 50 MHz clock frequency, while delivering 89.7% detection accuracy across 3 major poultry diseases. Experimental validation on a dataset of 1,200 poultry samples (sound, image, biosensor data) showed an overall disease classification accuracy of 91.3%, a false-positive rate of 4.8%, and an average latency of 1.4 ms per inference. Compared to cloud-based AI, the proposed VLSI system reduced energy consumption by 65% and response time by 72%, enabling continuous on-site monitoring. The proposed CMOS VLSI biosensor and on-hip accelator framework demonstrates c erhigh potential for scalable, low-power, and real-time poultry disease diagnostics. Future work includes integrating wireless transmission and deploying the architecture in farm-scale pilot studies to validate robustness under diverse environmental conditions.
The activity of flexible SERS substrates for aflatoxin B1 (AFB1) detection remains limited, stemming from challenges in the morphological control of active nanostructures. This study proposes a seed-mediated strategy for the simple and controllable synthesis of flexible Au-Ag NPs/PET substrates. By controlling the purity and size of the seeds, the morphology of the Au-Ag NPs can be tuned, providing a novel approach for the morphological control of Au-Ag NPs. Furthermore, the bayberry-like Au-Ag NPs/PET was used as an active substrate for SERS detection of AFB1 in milk. The substrate demonstrated excellent sensitivity. Notably, the bayberry-like Au-Ag NPs/PET enabled the rapid and quantitative detection of AFB1 over a concentration range from 10-6 M to 10-14 M. Clearly, the fabricated flexible bayberry-like Au-Ag NPs/PET holds considerable potential for applications in dairy product safety monitoring.
The increasing penetration of renewable distributed generators (RDGs) and electric vehicles (EVs) introduces significant operational complexity in modern microgrids, demanding efficient optimization and low-power control architectures. This paper proposes a CMOS-based optimal microgrid structuring framework integrated with intelligent energy management for coordinated RDG and EV operation. A low-power CMOS control architecture is designed to enable real-time sensing, load forecasting, and bidirectional EV charging control while minimizing hardware energy consumption. The structural optimization of generation units, storage systems, and EV charging stations is achieved using the Waterwheel Plant Algorithm (WPA), which ensures enhanced convergence and global search capability. The proposed framework optimally determines generator placement, power dispatch scheduling, and EV charging strategies under varying load and renewable intermittency conditions. Simulation results demonstrate a reduction in total operational cost by 18.6%, power loss minimization by 21.3%, and voltage deviation improvement by 15.8% compared to conventional particle swarm and genetic algorithm-based approaches. The CMOS-based controller further reduces control power consumption by approximately 32% under sub-thrshold operation. The integrated hardware-algorithm co-design eapproach enhances system reliability, scalability, and energy efficiency, making it suitable for next-generation smart microgrids with high EV penetration.
Electroluminescence (EL) imaging is a powerful nondestructive technique for evaluating carrier recombination and defect-related luminescence quenching in nanoengineered photovoltaic devices. However, automated classification of nanoscale and microscale defects in EL images remains challenging due to limited spatial resolution in deep features, class imbalance, and the inability of conventional convolutional networks to capture long-range carrier transport anomalies. Here, we propose Swin CBAM, a hybrid attention-driven architecture that integrates a hierarchical Swin Transformer with Convolutional Block Attention Modules (CBAM) to classify defects in EL images of crystalline silicon solar cells. The shifted window self-attention mechanism models spatially extended recombination regions, while CBAM sequentially refines channel-wise and spatial feature maps to emphasize defect-relevant optoelectronic signatures. To address the inherent class imbalance between defective and non-defective cells, we employ Focal Loss (y = 2.0) combined with physicsmotivated data augmentation. Evaluated on the public ELPV benchmark dataset (2, 624 EL images), our method achieves 95.24% classification accuracy, outperforming ResNet18 (87.05%), VGG16 (88.40%), and Vision Transformer ViT-B/16 (88.95%). Ablation studies show CBAM contributes the largest individual gain (+2.28%). With 29.12 million parameters and 8.5 ms inference time, the model balances optoelectronic feature discrimination and computational efficiency. These results demonstrate that transformer-based attention refinement effectively captures multiscale luminescence contrast mechanisms, offering a robust pathway for automated quality control in nanoelectronics and optoelectronic device manufacturing.
Early and precise detection of intracranial tumors is critical for improving diagnostic accuracy and patient survival in neuroimaging applications. Conventional image segmentation techniques often suffer from high computational complexity, poor noise tolerance, and increased power consumption, making them unsuitable for portable and embedded medical diagnostic systems. To address these limitations, this paper proposes a Low-Power CMOS Architecture for Quantum Fuzzy Clustered Image Segmentation (QFCIS) for efficient intracranial tumor detection from brain MRI images. The proposed framework integrates quantum-inspired fuzzy clustering with CMOS-based hardware optimization to enhance segmentation precision while minimizing energy consumption and computational latency. Initially, MRI images are preprocessed using adaptive normalization and contrast enhancement techniques to improve image quality. Then, quantum fuzzy clustering is employed for accurate tumor boundary extraction by utilizing probabilistic membership mapping and quantum state transition mechanisms. The segmentation algorithm is implemented through a low-power CMOS architecture designed with optimized transistor-levl switching logiand reduced leakage current pathways for hardware efficiency. Experimental analysis conducted on benchmark MRI datasets demonstrates that the proposed method achieves a segmentation accuracy of 98.74%, sensitivity of 97.92%, specificity of 98.31%, and Dice similarity coefficient of 97.85%, outperforming conventional fuzzy C-means and deep segmentation approaches. Additionally, the CMOS implementation reduces power consumption by 34.6%, area utilization by 29.4%, and processing latency by 26.8% compared to existing FPGA-based architectures. These results validate the effectiveness of the proposed model for real-time, portable, and energy-efficient intracranial tumor diagnosis systems.
Since perovskites emerged as exceptional light-harvesting materials, remarkable progress has been achieved in enhancing solar cell efficiencies. Building on this foundation, the manipulation of internal optical properties within perovskite architectures presents a promising route for further efficiency improvements. Herein, we demonstrate that embedding titanium nanocubes (TiCs)—which are relatively easy to fabricate—into perovskite thin films yields absorption enhancement comparable to that of titanium nanotriangles (TiTs), the latter being challenging to stabilize in ordered arrangements. This finding provides valuable guidance for metal-doping strategies in perovskite photovoltaics, offering a pragmatic alternative that balances performance enhancement with fabrication feasibility.
Cobalt sulfide (CoSx) nanocrystallites capped with mercaptoacetic acid (MAA) were synthesized via a colloidal precipitation method, with reaction temperature and precursor ratios systematically varied to control phase formation and particle size. Single-phase cubic Co9S8 nanocrystallites were obtained at optimized conditions, while the residual supernatant yielded mixed-phase cobalt sulfides upon secondary treatment. Comprehensive characterization using EDX, XRD, TGA, FTIR, UV-Vis, and photoluminescence spectroscopy revealed that first-precipitation nanocrystallites are cobalt-rich, thermally stable up to 1000 degrees C, and exhibit strong quantum confinement effects with tunable band gaps. Microwave-treated supernatant samples demonstrated increased sulfur content, modified surface coordinatio, and mixed-phase formation. The nanocrystallites displayed absorption edges in the near-infrared region and energy level splitting indicative of strong confinement. These findings highlight the potential of MAA-capped cobalt sulfide nanocrystallites for optoelectronic, photonic, and energy-related applications, including supercapacitors and photocatalytic hydrogen production.
The rapid modernization of smart renewable energy grids has increased dependency on distributed digital monitoring, edge intelligence, and interconnected communication networks, making grid infrastructures highly vulnerable to sophisticated cyber-attacks. Traditional centralized security mechanisms often suffer from latency, high computational overhead, and delayed response in real-time grid environments. To address these challenges, this paper proposes a Memristor-Based Nanoelectronic Edge Architecture for Smart Renewable Energy Grid Cybersecurity, integrating memristive nanoelectronic circuits with edge-based intelligent threat detection for secure and energy-efficient grid protection. The proposed architecture employs memristor crossbar arrays for ultra-low-power parallel data processing, enabling real-time anomaly detection and attack classification directly at distributed edge nodes within the grid. A hybrid lightweight deep learning classifier is embedded into the memristive hardware framework to identify false data injection, denial-of-service, spoofing, and intrusion-based attacks across renewable energy substations and IoT-enabled smart meters. Furthermore, adaptive threat prioritization and dynamic risk scoring mechanisms are incorporated to enhance decision-making accuracy under dynamic grid conditions. Experimental validation performed on benchmark smart grid cybersecurity datasets demonstrates that the proposed framework achieves 98.72% detection accuracy, 97.94% precision, 98.11% recall, and 98.02% F1-score, outperforming conventional CMOS and cloud-based security architectures. Additionally, the memristor-based implementation reduces computational latency by 34.6%, decreases power consumption by 41.3%, and improves edge inference throughput by 29.8% compared with traditional hardware accelerators. The proposed framework provides a scalable, lowpower, and intelligent cybersecurity solution for next-generation resilient renewable energy infrastructures.
The reliable operation of unmanned aerial vehicles (UAVs) in low-altitude economies requires robust obstacle avoidance, yet unimodal sensing fails under extreme lighting or weather. This paper presents a real-time obstacle avoidance system based on multimodal adaptive fusion of photoelectric and nano-radar sensors within a Transformer architecture. The system employs an end-to-end design with dual-stream heterogeneous feature extraction. A modified YOLOv5s processes photoelectric images for semantic features, while an adapted PointNet handles nano-radar point clouds for spatial geometry. A cross-modal multi-head selfattention mechanism dynamically fuses these features, overcoming the limitations of manually predefined modality weights. This design leverages the complementary nature of photoelectric sensors (high-resolution texture) and nano-radar (penetrating capability and precise depth), addressing nanoscale-level positioning challenges in dynamic environments. Experimental results on a custom Unity 3D dataset demonstrate that the system achieves a mean average precision (mAP) of 95.8% under ideal conditions. Notably, performance degradation under extreme interference (lare, backlight, rain, fog) is constrained to under 6%, compared to gover 30% for unimodal systems. The end-to-end response latency is 32.6 ms on an NVIDIA Jetson Xavier NX edge device, with a 99.2% average obstacle avoidance success rate. By enabling deep feature interaction and dynamic adaptive weighting, the proposed system significantly enhances environmental robustness and realtime perception, providing a reliable hardware-software co-design solution for autonomous UAV navigation in complex low-altitude airspace.
Image denoising in fibre-optic and nano-optoelectronic imaging systems is a critical challenge for highresolution signal recovery in minimally invasive medical diagnostics and industrial nanoscale inspection. Fibre bundles inherently introduce honeycomb-like modular artefacts and dark-spot defects due to discrete core sampling, while nano-optoelectronic sensors suffer from additive noise during weak-signal detection. This paper systematically investigates deep learning architectures to suppress such degradation without increasing hardware complexity. Three representative models are evaluated: a residual-based DnCNN-Plus, a multiscale U-Net, and a proposed hybrid Ultimate U-Transformer integrating global self-attention into a U-shaped encoder-decoder. Experimental results demonstrate that while all models achieve robust denoising, the Ultimate U-Transformer significantly outperforms convolutional counterparts in reconstruction fidelity. By combining long-range dependency modelling with local feature extraction, the hybrid architecture attains a peak PSNR of 24.6 dB and SSIM of 0.88, representing a substantial gain of approximately 6.6 dB over the DnCNNPlus baseline. Parameter sensitivity analysis furthr confirms the superior adaptability of Transformer-based processing for complex, non-uniform noise patterns common in fibre-optic and nano-optoelectronic image transmission. This work establishes a robust algorithmic and theoretical framework for real-time, high-fidelity image reconstruction, directly supporting the advancement of integrated nano-optoelectronic sensing and fibre-bundle imaging systems.
The electronic bandgap of semiconductor nanostructures fundamentally determines their suitability for optoelectronic applications including photovoltaics, light-emitting diodes, and photodetectors. However, traditional density functional theory (DFT) calculations, while accurate, remain computationally prohibitive for high-throughput screening of nanostructured materials required for device optimization. Here, we propose GATFormer, a novel Graph Attention Transformer architecture that synergistically combines graph attention mechanisms with Transformer self-attention to achieve state-of-the-art bandgap prediction for semiconductor nanostructures relevant to nanoelectronics. Our architecture introduces three key innovations tailored to nanoscale systems: (1) a hybrid graph-Transformer encoder that captures both local atomic bonding environments and long-range structural correlations critical for quantum-confined structures; (2) a nano-aware positional encoding scheme that explicitly distinguishes surface atoms from bulk-interior atoms and encodes quantum confinement directionality across different dimensioalitiesn (quantum wells, wires, and dots); and (3) a hierarchical pre-training strategy leveraging bulk crystal data before fine-tuning on nanostructure subsets. Extensive validation on the JARVIS-DFT dataset comprising approximately 75,000 materials demonstrates that GATFormer achieves a mean absolute error (MAE) of 0.072 eV for bandgap prediction, representing a 26.5% improvement over the current state-of-the-art ALIGNN model. Notably, for nanostructured materials that dominate next-generation optoelectronic device platforms, the improvement is even more pronounced, with MAE reduced from 0.128 eV to 0.094 eV. Our approach establishes a computationally efficient framework for structure-property prediction in nanoscale semiconductors, enabling rapid virtual screening of candidate nanostructures for targeted optoelectronic applications.
