Search Results (338)

Automatic techniques for analog integrated circuit layout design have been proposed in the literature for over four decades. However, as analog design moves into deep nanometer integration nodes, the increasing number of design rules, the influence of layout-dependent effects, congestion, and the impact of parasitic structures constantly challenges existing automatic layout generation techniques and keeps the pressure on for further improvement. At the time of writing, no automatic tool or flow has been established in the industrial environment, resulting in a time-consuming and difficult-to-reuse design process. However, very recently, machine and deep learning techniques started to offer solutions for problems not dealt with in the previous generation of automatic layout tools and are reshaping analog design automation. Therefore, this paper conducts a review of the most recent analog integrated circuit automatic layout techniques powered by machine and deep learning methods, covering placement, routing, and trends on post-layout performance estimation, as well as providing an actual, complete, and comprehensive guide for circuit designers and design automation developers. Full article

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demonstrate the feasibility of online VQC training within this architecture by approximating nonlinear functions in real time, thereby validating the potential of embedded QML for advanced signal processing and control applications. This approach provides a scalable and practical path toward real-time Quantum Reinforcement Learning (QRL) and other quantum-enhanced embedded controllers. The results validate the feasibility of real-time quantum emulation and establish a structured engineering design methodology for implementing trainable quantum machine learning (QML) models on embedded platforms, thereby enabling the development of deployable quantum-enhanced controllers. Full article

Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. The second key innovation lies in the creation of the Efficient Feature Fusion and Aggregation Network (EFAN), which integrates a lightweight Spatial-Channel Decoupled Downsampling (SCDown) module and three innovative fusion pathways. This achieves substantial parameter reduction while effectively integrating shallow detail features with deep semantic features, preserving critical defect information across different feature levels. Finally, the Shape-IoU loss function is incorporated, focusing on bounding box shape and scale for more accurate regression and enhanced defect localization precision. Experiments on the enhanced Peking University PCB defect dataset show that PCES-YOLO achieves a mAP50 of 97.3% and a mAP50–95 of 77.2%. Compared to YOLOv11n, it shows improvements of 3.6% in mAP50 and 15.2% in mAP50–95. When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50–95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. These results indicate that PCES-YOLO outperforms these algorithms in terms of detection accuracy and efficiency, making it a promising high-precision and efficient solution for PCB defect detection in industrial settings. Full article

Model order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment matching (MM) techniques are widely adopted in industrial tools due to their computational efficiency and ability to preserve passivity in RLCk models. Typically, MM approaches based on the rational Krylov subspace (RKS) are employed to produce reduced-order models (ROMs). However, the quality of the reduction is influenced by the selection of the number of moments and expansion points, which can be challenging to determine. This underlines the need for advanced strategies and reliable convergence criteria to adaptively control the reduction process and ensure accurate ROMs. This article introduces a frequency-aware multi-point MM (MPMM) method that adaptively constructs an RKS by closely monitoring the ROM transfer function. The proposed approach features automatic expansion point selection, local and global convergence criteria, and efficient implementation techniques. Compared to an established MM technique, MPMM achieves up to 16.3× smaller ROMs for the same accuracy, over 99.18% reduction in large-scale benchmarks, and up to 4× faster runtime. These advantages establish MPMM as a strong candidate for integration into industrial parasitic extraction tools. Full article

The Internet of Things (IoT) is one of the pivotal technologies driving the digital transformation of industry, business, and personal life. Along with new opportunities, the exponential growth of IoT devices also brings environmental challenges, driven by the increasing accumulation of e-waste. This paper introduces a novel, compact, cubic-shaped rectenna with a 3D-printed composite substrate featuring five identical patches. The design aims to integrate RF energy harvesting technology with eco-friendly materials, enabling its application in powering next-generation sustainable IoT systems. Due to its symmetrical design, each patch antenna achieves a bandwidth of 130 MHz within the frequency range of 2.4 GHz to 2.57 GHz, with a maximum efficiency of 60.5% and an excellent isolation of below −25 dB between adjacent patch antennas. Furthermore, measurements of the rectifier circuit indicate a maximum conversion efficiency of 33%, which is comparable to that of other rectennas made on 3D-printed substrates. The proposed visually unobtrusive design not only enhances compactness but also allows the proposed rectenna to harvest RF energy from nearly all directions. Full article

The application of oxygen sensors based on Tunable Diode Laser Absorption Spectroscopy (TDLAS) in the industrial field has received extensive attention. However, most of the existing studies construct detection systems using discrete devices, making it difficult to apply them in the industrial field. In this work, through the optimization of the sensor circuit, the size of the core components of the sensor is reduced to 7.8 × 7.8 × 11.8 cm³, integrating the laser, photodetector, and system control circuit. A novel integrated optical path design is proposed for the optical mechanical structure, which enhances the structural integration and long-term optical path stability while reducing the system assembly complexity. The interlocking design of the laser-driven digital-to-analog converter (DAC) and photocurrent acquisition analog-to-digital converter (ADC) reduces the requirements of the harmonic signal extraction for the system hardware. By adopting a high-precision ADC and a high-resolution pulse-width modulation (PWM), the peak-to-peak value of the laser temperature control noise is reduced to 2 m°C, thereby reducing the detection noise of the sensor. This oxygen detection system has a minimum response time of 0.1 s. Under the condition of a 0.5 m detection optical path, the Allan variance shows that when the integration time is 5.6 s, the detection limit reaches 53.4 ppm, which is ahead of the detection accuracy of similar equipment under the very small system size. Full article

Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the Kopli Solar Power Plant, Estonia, by integrating controlled laboratory simulations with extensive field monitoring. Initially, detailed laboratory experiments were conducted to replicate critical DC-side short-circuit scenarios, particularly focusing on negative DC input terminal faults. The results consistently showed these faults rapidly escalating into multi-phase short-circuits and sustained ground-fault arcs due to inadequate internal protection mechanisms, semiconductor breakdown, and delayed relay response. Subsequently, extensive field-based waveform analyses of multiple inverter failure events captured identical fault signatures, thereby conclusively validating laboratory-identified failure mechanisms. Critical vulnerabilities were explicitly identified, including insufficient isolation relay responsiveness, inadequate semiconductor transient ratings, and ineffective internal insulation leading to prolonged arc conditions. Based on the validated findings, the paper proposes targeted inverter design enhancements—particularly advanced DC-side protective schemes, rapid fault-isolation mechanisms, and improved internal insulation practices. Additionally, robust operational and monitoring guidelines are recommended for industry-wide adoption to proactively mitigate future inverter failures. The presented integrated methodological framework and actionable recommendations significantly contribute toward enhancing inverter reliability standards and operational stability within grid-connected photovoltaic installations. Full article

As urbanization progresses at an accelerating pace, the depletion of natural resources and environmental degradation are becoming increasingly severe. Constructing ecological security patterns (ESPs) has become a crucial strategy for mitigating environmental stress and promoting sustainable social development. Currently, the methods for constructing ESPs remain under exploration. Particularly, in the identification of ecological sources, insufficient emphasis has been placed on trade-offs among ecosystem services (ESs). This study focuses on Liaoning Province, situated in China’s northeast revitalization area—a region with a developed heavy industry and abundant ecological resources. The InVEST model was employed to assess ESs, and the ordered weighted average (OWA) method was utilized to identify ecological sources. By integrating both natural and social factors, the ecological resistance surface was constructed, and circuit theory was applied to determine ecological corridors, ultimately leading to the development of an ESP. The results show that (1) between 2010, 2015, and 2020, water yield continued to increase, habitat quality continuously declined, soil conservation tended to decrease and then gradually increase, and carbon storage tended to increase and then decrease. The four ESs show similar spatial features, characterized by elevated levels in the eastern and western areas and a comparatively reduced level in the central region; (2) a total of 179 ecological sources were identified, covering 26,235.34 km². The overall distribution showed a concentration in the east, with a fragmented and dispersed pattern in the southwest. The identification of 435 ecological corridors, with an overall length totaling 8794.59 km, resulted in a network-like distribution pattern. Additionally, 65 ecological pinch points and 67 barrier points were identified; and (3) a “four zones, three corridors, and two belts” pattern of ecological protection and restoration has been proposed. The findings offer valuable insights for Liaoning Province and other rapidly developing regions facing escalating environmental pressures. Full article

This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings. Full article

Conventional magnetorheological fluids (MRFs) exhibit a constrained shear strength that restricts their deployment in high-performance damping systems. This study introduces a dual-axis innovation strategy combining material science and device physics to fundamentally redefine MRF capabilities. We develop a hierarchical particle architecture through the controlled integration of micro/nano-sized carbonyl iron particles (CIPs), enhanced by polyethylene glycol/oleic acid surface engineering to optimize magnetic chain formation and interfacial bonding. The engineered MRF demonstrates a shear yield strength of 99.6 kPa at 0.757 T, surpassing conventional single-component MRFs by a significant margin. Integrated with a self-decoupling damper that isolates magnetic flux from mechanical motion, this synergistic design achieves exceptional force modulation: damping forces scale from 281.5 kN (5 mm stroke) to 300 kN (60 mm stroke), with current-regulated adjustability factors reaching 3.34. The system exhibits substantial improvements in both maximum damping force (93.9 kN enhancement) and energy dissipation efficiency compared to standard MRF dampers. Through co-optimization of the particle architecture and magnetic circuit design, this work establishes new performance benchmarks for smart fluid technology. The achieved force capacity and dynamic response characteristics directly address critical challenges in seismic engineering and industrial vibration control, where extreme load-bearing requirements demand simultaneous high strength and tunable damping capabilities. Full article

Globally, there are now more than 19 billion connected Internet of Things (IoT) devices, which are fostering innovation across various sectors, including industry, healthcare, education, energy, and agriculture. With the rapid expansion of IoT devices, there is an increasing demand for sustainable, self-powered, eco-friendly solutions for next-generation IoT devices. Harvesting and converting radio frequency (RF) energy through rectennas is being explored as a potential solution for next-generation self-powered wireless devices. This paper presents a methodology for designing, optimizing, and fabricating a flexible rectenna for RF energy harvesting in the 5G lower mid-band and ISM 2.45 GHz band. The antenna element has a tree form based on a fractal structure, which provides a small size for the rectenna. Furthermore, to reduce the rectenna’s environmental impact, we fabricated the rectenna on a substrate from biodegradable materials—natural rubber filled with rice husk ash. The rectifier circuit was also designed and fabricated on the flexible substrate, facilitating the seamless integration of the rectenna in next-generation low-power IoT devices. The numerical analysis of the parameters and characteristics of rectenna elements, based on the finite-difference time-domain method, demonstrates a high degree of agreement with the experimental results. Full article

As the performance requirements for printed circuit boards (PCBs) in electronic devices continue to increase, reliable defect detection during PCB manufacturing is vital. However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight detection algorithm for PCB defect detection based on improved YOLOv12 architecture. In the Backbone, a Median-enhanced Channel and Spatial Attention Block (MECS) expands the receptive field through median enhancement and depthwise convolution to generate attention maps that effectively capture subtle defect features. In the Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features through weighted integration, enhancing feature utilization and focus on defect regions. Moreover, the original YOLOv12 loss function is replaced with the Slide Alignment Loss (SAL) to improve bounding box localization and detect complex defect types. Experimental results demonstrate that MAS-YOLO significantly improves mean average precision (mAP) and frames per second (FPS) compared to the original YOLOv12, fulfilling real-time industrial detection requirements. Full article

This research investigates the integration of quantum hardware-assisted security into critical applications, including the Industrial Internet-of-Things (IIoT), Smart Grid, and Smart Transportation. The Quantum Physical Unclonable Functions (QPUF) architecture has emerged as a robust security paradigm, harnessing the inherent randomness of quantum hardware to generate unique and tamper-resistant cryptographic fingerprints. This work explores the potential of Quantum Computing for Security-by-Design (SbD) in the Industrial Internet-of-Things (IIoT), aiming to establish security as a fundamental and inherent feature. SbD in Quantum Computing focuses on ensuring the security and privacy of Quantum computing applications by leveraging the fundamental principles of quantum mechanics, which underpin the quantum computing infrastructure. This research presents a scalable and sustainable security framework for the trusted attestation of smart industrial entities in Quantum Industrial Internet-of-Things (QIoT) applications within Industry 4.0. Central to this approach is the QPUF, which leverages quantum mechanical principles to generate unique, tamper-resistant fingerprints. The proposed QPUF circuit logic has been deployed on IBM quantum systems and simulators for validation. The experimental results demonstrate the enhanced randomness and an intra-hamming distance of approximately 50% on the IBM quantum hardware, along with improved reliability despite varying error rates, coherence, and decoherence times. Furthermore, the circuit achieved 100% reliability on Google’s Cirq simulator and 95% reliability on IBM’s quantum simulator, highlighting the QPUF’s potential in advancing quantum-centric security solutions. Full article

This paper proposes an automated system integrating infrared thermography (IRT) and computer vision for air leak detection and localization in end-of-line (EOL) testing stations. This system consists of (1) a leak tester for detection and quantification of leaks, (2) an infrared camera for real-time thermal image acquisition; and (3) an algorithm for automatic leak localization. The python-based algorithm acquires thermal frames from the camera’s streaming video, identifies potential leak regions by selecting a region of interest, mitigates environmental interferences via image processing, and pinpoints leaks by employing pixel intensity thresholding. A closed circuit with an embedded leak system simulated relevant leakage scenarios, varying leak apertures (ranging from 0.25 to 3 mm), and camera–leak system distances (0.2 and 1 m). Results confirmed that (1) the leak tester effectively detected and quantified leaks, with larger apertures generating higher leak rates; (2) the IRT performance was highly dependent on leak aperture and camera–leak system distance, confirming that shorter distances improve localization accuracy; and (3) the algorithm localized all leaks in both lab and industrial environments, regardless of the camera–leak system distance, mostly achieving accuracies higher than 0.7. Overall, the combined system demonstrated great potential for long-term implementation in EOL leakage stations in the manufacturing sector, offering an effective and cost-effective alternative for manual inspections. Full article