Search Results (337)

The continuous scaling of semiconductor technology is facing increasing physical limitations, making conventional geometry-driven scaling insufficient for future ultra-scale computing systems. Electronic-photonic integrated circuits (EPICs) provide a promising approach to overcome interconnect and bandwidth bottlenecks; however, existing EPIC design methodologies still suffer from fragmented optimization across different abstraction levels. In this work, we propose a τ-Law-driven design methodology based on temporal scaling for ultra-scale electronic-photonic integrated circuits. By introducing the time constant τ as a unified optimization objective, the proposed framework enables coordinated optimization across device, circuit, chip, and system levels. A mathematical formulation is established to integrate electronic interconnect delay, optical propagation delay, and electro-optical/opto-electrical conversion delays into a unified temporal optimization model. A quantitative evaluation methodology based on representative parameters reported in previous EPIC studies is further developed to analyze the effectiveness of τ-oriented optimization. The proposed approach is further validated using a proof-of-concept electronic-photonic link, where different configurations are experimentally compared in terms of end-to-end latency. The quantitative and experimental results show that coordinated reduction in multiple latency contributions can reduce system-level latency compared with conventional independent optimization, providing a potential pathway beyond geometry-driven scaling toward scalable and automated EPIC design for future photonic computing and heterogeneous integration systems. Full article

This paper proposes a compact and tunable plasmon modulator based on a metal-grating/graphene hybrid structure, aiming to overcome the critical bottlenecks of conventional modulators in terms of integration density and dynamic tunability. The device employs two metallic gratings that generate a periodic alternating electric field in the graphene when opposite voltages are applied across the two gratings, resembling an interdigitated electrode configuration. This electric field spatially modulates the carrier density in the graphene layer, forming an equivalent Bragg grating whose duty cycle and groove depth can be dynamically tuned by adjusting the voltage amplitude, thereby enabling modulation of propagating surface plasmon polaritons in graphene. The proposed modulator, based on dual-parameter control of the Bragg grating, achieves a 10 dB enhancement in maximum modulation depth over single-parameter (groove depth) modulation, with a compact footprint of 1 μm × 0.6 μm × 1 μm, a center-wavelength tuning range of 1.0 μm, and an FWHM tuning range of 0.55 μm. This compact and tunable modulator holds significant promise for applications in mid-infrared communication and integrated photonic chips. Full article

To meet the demand for compact, low-power on-chip electro-optic modulators for high-density photonic integration in the post-Moore era, this paper proposes an on-chip integrated electro-optic modulation structure that combines an embedded waveguide–electrode configuration with a c-axis-oriented BaTiO₃ crystal thin film and a one-dimensional Si₃N₄ topological photonic crystal. The proposed structure tunes the equivalent refractive index by means of periodic rectangular holes patterned in a Si₃N₄ loaded-strip waveguide, and constructs two one-dimensional photonic crystal systems with opposite topological invariant signs under the Su–Schrieffer–Heeger (SSH) model. When the two systems are spliced, a localized topological interface state resonance is formed within their common band gap, thereby enhancing optical field confinement. An embedded electrode configuration is further introduced to improve the electric field distribution within the BaTiO₃ crystal thin film, and the electro-optic modulation performance is improved by optimizing key parameters, including the rectangular hole dimensions, lattice constant, number of periods, electrode embedding depth, and the Si₃N₄ waveguide height. The results show that the proposed structure yields a narrow-linewidth topological interface state near 1550 nm and achieves an equivalent resonant-wavelength tuning efficiency of approximately 300 pm/V at a modulation voltage of 10 V, with a modulation region only 61 μm long. The structure substantially shortens the device dimensions and provides a feasible design scheme for realizing ultra-compact, high-efficiency on-chip electro-optic modulators. Full article

Accurate, real-time temperature monitoring of traction motors, battery packs, and power electronic modules is a critical requirement for the safety, efficiency, and long-term reliability of electric vehicles (EVs), particularly on the curved, space-constrained, and vibration-prone surfaces found on motor end-windings, battery-module casings, and busbar assemblies. To meet the needs of such automotive curved-surface applications, as well as wearable devices and flexible electronic skin, this paper presents complementary metal-oxide-semiconductor (CMOS) temperature sensor. Its core includes a CMOS sensor chip with an integrated bandgap reference circuit and dual-path electrostatic discharge (ESD) protection, combined with flexible printed circuit board (FPCB/FPC) for conformal mounting on curved surfaces. Circuit-level simulation predicts a typical reference-voltage temperature coefficient of 20 ppm/°C over −40 to 125 °C, while experimental temperature characterization yields a temperature output (TEMP) sensitivity of approximately 5.0 mV/°C over the reported temperature range. Absolute temperature error and linearity are not claimed as independently verified performance metrics in the present revision because the currently available experimental documentation does not preserve the reference-temperature calibration traceability, repeated-measurement information, measurement-uncertainty analysis, or calculation definitions required to substantiate the previously reported ±1 °C and 0.99% values. After flexible integration and 100 bending cycles at a 10 mm radius, the reference-voltage variation remains below 0.1%, while the reported temperature-equivalent TEMP-output shift remains within ±0.5 °C under the tested laboratory conditions. These results demonstrate short-term laboratory bending stability and temperature-sensing performance under the tested conditions rather than long-term fatigue or automotive vibration qualification. The proposed sensor therefore demonstrates potential for curved and space-constrained thermal-monitoring applications, including permanent magnet synchronous motor (PMSM) stator windings and battery-module surfaces, while validation on actual EV components and formal automotive qualification remain necessary for production deployment. Full article

Electrical discharge machining (EDM) of thermal barrier coatings (TBCs) on curved superalloy components faces substantial challenges. These challenges stem from the significant material property mismatch between ceramic coatings and metallic substrates, as well as the effect of workpiece curvature. Conventional fixed-parameter control can cause coating ablation, entrance edge chipping, wall microcracking, and interfacial delamination. This study develops an adaptive EDM control system integrating high-speed discharge state detection and intelligent parameter adjustment. A discharge-state classification method based on voltage thresholds, discharge delay time, and discharge duration is adopted in this system. With a field-programmable gate array (FPGA) controller possessing nanosecond-level response speed and after hardware delay is considered, real-time state identification is achieved on the microsecond timescale. The relative high-resistance spark rate (RHSR) is introduced as a machining progress indicator to distinguish machining stages. According to the identified machining stage and discharge state, the electrical parameters, arc plasma energy control, short-circuit interruption, and electrode servo motion are adjusted adaptively. Comparative experiments show that the proposed strategy markedly reduces carbon deposition, suppresses entrance flaring and edge chipping, decreases wall microcracking, and prevents interfacial delamination while maintaining machining efficiency comparable to conventional fixed-parameter machining. Full article

Difficult-to-machine materials, including titanium alloys, nickel-based superalloys, hardened and high-strength steels, stainless steels, and fiber-reinforced composites, are widely used in advanced manufacturing but impose severe thermo-mechanical–chemical loads on cutting tools, resulting in progressive wear and degradation, localized damage, and, in severe cases, catastrophic failure. This review summarizes recent advances in tool condition monitoring and prognostics following the framework of material characteristics–tool deterioration mechanisms–condition sensing–predictive modeling–manufacturing decision making. The relationships between material properties and tool condition deterioration are first discussed, distinguishing progressive wear mechanisms, such as abrasion, adhesion, diffusion, and oxidation, from thermally induced degradation and localized damage phenomena such as cracking, coating delamination, and edge chipping. Direct tool measurement and indirect process-response monitoring based on force and torque, vibration and acoustic signals, thermal signals, and machine-tool electrical signals are then reviewed, while machining-quality characteristics are treated separately as machining-outcome-based condition indicators. Multisource information fusion is further discussed for integrating complementary tool condition information from these different sources. Tool condition assessment and prognosis are further examined in terms of wear estimation, tool-condition state identification, remaining useful life prediction, and catastrophic-failure risk prediction. Physics-based, data-driven, and hybrid models are compared with respect to accuracy, interpretability, uncertainty, and generalization. Finally, industrial deployment through online inference, edge-cloud collaboration, CNC integration, and digital-twin-supported closed-loop tool health management is discussed, together with key challenges in limited-data learning, cross-condition generalization, multimodal fusion, uncertainty quantification, standardized evaluation, and sustainable manufacturing. Full article

The rapid expansion of autonomous microsystems, implantable sensors, wireless sensor nodes, Internet-of-Things devices, distributed electronics, and heterogeneous system-on-chip platforms has intensified the demand for compact electrochemical energy-storage systems that can be integrated directly with microfabricated devices. Among the various negative electrode materials, silicon is particularly attractive for miniaturized lithium-ion batteries because of its high theoretical lithium-storage capacity, abundance, compatibility with mature semiconductor processing, and direct availability as both an active material and a structural platform. However, the practical implementation of silicon anodes in on-chip and miniaturized batteries remains difficult because lithiation-induced volume expansion, fracture, unstable solid-electrolyte interphase formation, loss of electrical contact, and process-integration constraints become more severe as the battery footprint is reduced to the microscale. In contrast to conventional slurry-cast silicon electrodes, silicon anodes for microbatteries can exploit microfabrication strategies such as thin-film deposition, photolithography, deep reactive ion etching, metal-assisted chemical etching, nanoimprint lithography, laser patterning, template-assisted growth, atomic layer deposition, and wafer-level encapsulation. These methods enable deterministic control over electrode geometry, areal loading, porosity, current-collector contact, diffusion length, mechanical compliance, interfacial chemistry, and compatibility with complementary metal-oxide-semiconductor and microelectromechanical-system platforms. This review summarizes the recent progress in microfabrication strategies for silicon anodes in on-chip and miniaturized batteries, emphasizing the relationship between the process route, electrode architecture, mechanical stability, electrochemical performance, and manufacturability. Full article

Ion implantation is one of the key processes in semiconductor device and integrated circuit manufacturing. It injects charged ions into the wafer surface layer via an accelerating electric field, enabling precise modulation of the material’s electrical properties. As the “power heart” of an ion implantation system, the high-voltage power supply directly determines the energy consistency, spatial distribution uniformity, and process repeatability of the ion beam. As technology nodes continue to shrink to 3 nm and beyond, the ripple coefficient of the high-voltage power supply has become a critical concern. Even millivolt-level ripple can cause ion energy dispersion, leading to implantation depth deviations and non-uniform doping concentrations, thereby degrading chip yield and device reliability. To address this challenge, this paper proposes a high-frequency high-voltage generator based on an IPOS (input-parallel output-series) structure. It uses SiC MOSFET devices and adopts a PWM phase-shift control strategy. Simulation results predict an 86% reduction in peak-to-peak output ripple under interleaved phase-shift control, while experimental results confirm balanced module voltages, 60° interleaved resonant currents, and zero-voltage-switching (ZVS) operation at 100 kV/10 mA output. The switching frequency is tripled compared with conventional IGBT-based designs, reaching 222 kHz. These findings confirm the generator’s effectiveness in meeting the stringent low ripple requirements of advanced ion implantation processes. Full article

The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits. Full article

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

High-efficiency mixing and precise droplet generation are essential for the broad application of microfluidic chips in biology, chemistry, and medicine, with integrated micromixer and microdroplet generator systems playing an increasingly important role in diagnostics and drug detection. This review offers a comprehensive and systematic overview of micromixers and microdroplet generators, covering their classification, working principles, performance characterization, and integration strategies. Passive and active micromixers, the latter employing pressure, electric, acoustic, magnetic, and thermal fields to enhance mixing, are summarized with a critical discussion of their advantages, limitations, and structural optimization. Various droplet generation methods, including crossflow, flow focusing, coflow, step emulsification, and active techniques, are examined alongside key parameters affecting droplet size, monodispersity, and generation frequency. Performance characterization metrics for both components, including mixing efficiency, mixing time, pressure drop, droplet size distribution, generation frequency, and stability, are also discussed. The review then focuses on integration, emphasizing two primary architectural strategies: mixing reagents before encapsulation and inducing mixing within droplets after formation. Synergistic applications in single cell analysis, materials synthesis, and drug screening are presented. Notably, unlike previous reviews that treat micromixers or droplet generators separately, this review uniquely emphasizes architectural integration and critically evaluates the associated trade-offs. Current challenges and future directions, including material selection, fabrication techniques, and practical application-oriented considerations, are also addressed. Full article

Biogenic amines (BAs) are nitrogenous compounds formed by microbial decarboxylation of amino acids in protein-rich foods. Their accumulation indicates spoilage and poses health risks. Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography (GC) are sensitive but time-consuming, limiting on-site use. Rapid technologies based on specific recognition molecules offer feasible alternatives for real-time monitoring. This review summarizes five categories of recognition elements: antibodies, aptamers, molecularly imprinted polymers (MIPs), enzymes, and peptides for BA detection in foods. These elements convert BA concentrations into optical, electrical, or colorimetric signals, establishing a complete biosensing chain. Integration with portable platforms (lateral flow assays (LFAs), microfluidic chips, smart labels, and smartphone devices) is also discussed. Recognition-element-based sensing enables high-selectivity and rapid monitoring of BAs in foods. Antibody/aptamer systems excel in specific histamine detection, enzyme platforms in rapid total amine assessment, and MIPs in chemical stability and matrix tolerance. Yet practical application is limited by poor selectivity for similar amines, matrix interference, insufficient real-food validation, and device standardization. Our future focus will be on AI-assisted design, multi-target arrays, smartphone quantification, and IoT-enabled freshness monitoring. Full article

The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz–1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode–skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole–Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 $\mathsf{\Omega }$ across the investigated frequency range, Cole–Cole parameters consistent with reference values, and strong linear correlation ($R^2=0.97$) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact ($1.5\times 1.5$) mm² system-on-chip with low power consumption (1.5 mW). Full article

Tear analysis plays a crucial role in the early screening and diagnosis of ophthalmic diseases. However, conventional methods are often limited by poor real-time performance, low portability, and insufficient capability for multi-parameter detection. Here, we present a bioinspired triboelectric electronic tongue integrated with a microfluidic chip for multimodal detection of tear pH and disease-related biomarkers. The system combines three triboelectric nanogenerator (TENG) modes, including droplet-based, dual-electrode sliding, and single-electrode sliding configurations. The droplet-based TENG converts gravitational potential energy into electrical energy, generating a maximum output voltage of 65 V. The sliding TENG further expands the sensing dimensions by characterizing droplet flow behavior and viscosity-related properties. Benefiting from the high sensitivity of the dual-electrode mode and the waveform differentiation capability of the single-electrode mode, the platform enables enhanced sample discrimination. After optimizing key parameters, including droplet height and chip inclination angle, the output stability errors for all three TENG modes were maintained within ±10%. Combined with a random forest algorithm, the multimodal sensing system achieved a classification accuracy exceeding 96.6% for artificial tears with different pH values. Moreover, distinct electrical response patterns were observed for ophthalmic disease-related biomarkers, including Lysozyme, Interleukin-6 (IL-6), and Chlamydia, demonstrating excellent type identification and concentration detection capability. This work provides a self-powered and miniaturized strategy for intelligent tear analysis and multiple-parameter sensing, offering significant potential for ophthalmic disease diagnosis. Full article

Microscopic magnetic stimulation (MSTI) induces electric fields without direct charge injection and can shape localized field gradients with asymmetric micro-coils. Most demonstrations still rely on external drivers, off-chip hardware, or separated coil validation, so the CMOS integration boundary remains poorly characterized. This work presents a fabricated $2\times 1 {\mathrm{mm}}^2$ $0.18 \mathsf{\mu }\mathrm{m}$ CMOS magnetic-stimulation SoC that co-integrates ASK-compatible command decoding, FSM and register-based parameter control, a programmable current–voltage–current triangular driver, and a bent top-metal micro-coil, and it characterizes the on-chip driver-to-coil path together with a substrate-aware field model. Sensing-load reconstruction confirms command-to-waveform programmability, including duration-window decoding, burst-count control, and polarity reversal, with measured slew targets that give a peak current of $I_{\mathrm{pk}}=3.72$–$21.6 \mathrm{mA}$. A quantitative comparison contrasts the current-mode triangular driver with conventional electrode stimulators, a coil-impedance measurement shows the coil stays resistive across 1 to 10 MHz, and the measured total SoC power is about 41 mW. Substrate-aware simulation at a $15 \mathsf{\mu }\mathrm{m}$ target plane shows that the grounded p-substrate retains $35.1$–$40.5\%$ of the no-substrate peak x-directed field-gradient metric. The prototype establishes this electrical programmability and the substrate-aware gradient-transfer loss as a compact design-margin metric for CMOS-integrated magnetic stimulation. Direct biological activation is not claimed and is left to future in vitro validation. Full article