Search Results (1,706)

As the requirements in fields such as automobile, high-frequency signal generation, and medical applications, the resolution of time-to-digital converters (TDCs) has been pushed to the picosecond and sub-picosecond levels. In this study, a Vernier TDC was investigated with a time resolution less than the signal transition in the circuit. As generally happens in TDCs or Analog-to-Digital Converters (ADCs), bubble errors are found to degrade the resolution of their output codes. The bubble errors are usually attributed to non-idealities and mismatches in the circuits. Since the input time difference in high-resolution TDCs is much smaller than the signal transition time with the existence of bubble errors, it is an issue to determine the corresponding thermometer code from the output bit string of interleaved 0 s and 1 s. In our exploration, a Xilinx FPGA was employed to implement a Vernier Delay Line (VDL) for the TDC. In this timing-sensitive design, the timing difference between the two paths mainly comes from the interconnects rather than the Look-Up Table (LUT) devices. Timing constraints and regular placement were also imposed in addition to the simple Register Transfer Level (RTL) codes. Since the nature of uncertainty, a statistical model was proposed to analyze the output bit patterns. Three methods were employed to determine the output thermometer code. The first would count the total number of 1 s in the output. The second is to detect the position of the last 1. And the third is to detect the first 0 in the output bit string. The obtained results showed that these three methods were almost equivalent in the statistical outputs. The time resolution of our FPGA-based VDL can be around 5 ps in our measurement. According to our model, the transition time in the FPGA circuit was estimated as 100 ps. This result is reasonable for a chip made of 28 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. For the study of the linearity of our VDL, its differential nonlinearity (DNL) was less than ±2 LSB. The code-density-like analysis also shows the nonlinearity of this VDL. It was also found that the methods detecting the last 1 and the first 0 were sensitive to bit failures. In summary, for this study, it is confirmed that the three conversion methods are equivalent, and we found that detecting the last 1 or the first 0 was sensitive to bit defects or mismatches. Full article

Background/Objectives: Embryo implantation is a highly regulated, multistep process requiring precise synchronization between a developmentally competent blastocyst and a receptive endometrium. Despite advances in reproductive medicine, implantation failure remains a major limiting factor in assisted reproductive technology (ART), particularly in cases of recurrent implantation failure (RIF). This review aims to summarize current knowledge on the molecular, cellular, and immunological mechanisms governing embryo–endometrial interaction and to evaluate contemporary strategies for optimizing implantation outcomes. Methods: This narrative review synthesizes the current literature on embryo implantation, including studies addressing uterine receptivity, etiological factors contributing to implantation failure, and emerging diagnostic and therapeutic approaches. The review integrates findings from molecular biology, clinical ART practices, and bioengineering-based models. Key areas include transcriptomic tools such as endometrial receptivity analysis, time-lapse imaging, artificial-intelligence-based embryo selection, and advanced in vitro models (e.g., microfluidic “womb-on-a-chip” systems and three-dimensional embryo–endometrial platforms). The literature was identified through major biomedical databases, following a structured but non-systematic approach. Results: Implantation success is dependent on a complex interplay of hormonal regulation, gene expression, immune modulation, and embryo quality. Disruption of uterine receptivity during the window of implantation is a critical contributor to infertility and RIF. Multiple factors—including genetic abnormalities, maternal age, lifestyle influences, immunological imbalance, uterine pathology, and chronic endometrial conditions—are implicated in implantation failure. Emerging technologies, such as AI-assisted embryo selection, transcriptomic profiling, and advanced in vitro implantation models, provide enhanced insight into implantation dynamics and offer potential for improved clinical outcomes. Conclusions: Advances in understanding embryo implantation and the development of innovative diagnostic and therapeutic technologies hold significant promise for improving reproductive success. However, further research, validation, and standardization are required before these approaches can be fully integrated into routine clinical practice. A more personalized and mechanism-based approach to implantation may ultimately enhance ART outcomes and reduce the burden of infertility. Full article

The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems. Full article

DNA extraction is a critical prerequisite for reliable downstream analyses such as Polymerase Chain Reaction (PCR), sequencing, and genotyping. Conventional methods often require labor-intensive protocols, large sample volumes, or costly automation. Microfluidic approaches offer an alternative by reducing reagent consumption and enabling faster, more integrated workflows. Here, we present a passive lab-on-a-chip device that performs DNA extraction from complex biological media and enables subsequent on-chip single-molecule analysis. The chip integrates a magnetophoresis-based solid-phase extraction module with a fluorescence detection section capable of quantifying DNA molecules in microchannels and visualizing stretched molecules in nanochannels. The multi-level micro/nanofluidic architecture is fabricated in polymer using a single-step nanoimprinting process with a total manufacturing time of two minutes per chip, enabling scalable production. As a proof of concept, the device extracted DNA from samples spiked into buffer or plasma. On-chip transfer efficiency of DNA–bead complexes to the elution buffer reached 86%, and quantitative analysis of the recovered liquid showed an overall extraction efficiency of 40% (including DNA recovery off-chip), with intact 48 kbp DNA confirmed in both micro- and nanochannel measurements. This platform offers a promising foundation for point-of-care and point-of-interest applications, where integrated DNA extraction and analysis can reduce sample loss and support streamlined, automated workflows. Full article

Forensic biotechnology is a rapidly evolving interdisciplinary field integrating molecular biology, genomics, and data science to address complex investigative challenges. Its applications span diverse domains, including criminalistics, food authentication, environmental monitoring, and bioterrorism preparedness. Advanced technologies such as Next-Generation Sequencing (NGS), CRISPR-Cas biosensors, and Artificial Intelligence (AI) play pivotal roles in modern diagnostics. NGS and eDNA revolutionize genetic profiling and ecological tracking, while microbiome analysis provides crucial insights into post-mortem intervals, cause of death, and geolocation. Simultaneously, CRISPR-based methods enable ultra-rapid pathogen detection, nanobiotechnology facilitates portable Lab-on-a-Chip (LOC) DNA analysis, and AI-driven algorithms optimize the interpretation of complex genomic mixtures and epigenetic age estimation. Despite these breakthroughs, significant challenges persist, including the strict legal admissibility of novel methodologies, the “black-box” dilemma in AI, ethical concerns regarding genetic privacy, and the critical need for global standardization. This review critically examines current biotechnological progress and future prospects, emphasizing the necessity of interdisciplinary collaboration to ensure reliable, accurate, and ethically sound forensic practices. Full article

Three-dimensional (3D) cardiac models, including spheroids, organoids, and organ-on-chips, are advanced systems for studying human physiology, disease, and drug responses with greater biological relevance than 2D models. As their use expands in biomedical research, tissue engineering, and regenerative medicine, reliable preservation methods are needed. However, cryopreservation often fails to protect 3D systems due to limited cryoprotectant penetration, ice formation, and mechanical stress during freezing and thawing. Room-temperature (RT) preservation has emerged as a promising alternative for short-term transport. This study evaluated a RT-based transport medium (CellShip^®) for preserving cardiac organoids for up to seven days, compared with conventional cryopreservation using slow-freezing in Cryostor^®CS10. Viability and functionality were assessed using apoptosis, ATP levels, beating activity, proliferation, and size. During maturation, organoids showed increased size, ATP levels, and beating capacity. Cryopreservation reduced size, proliferation, ATP levels, and altered beating, while increasing apoptosis. In contrast, RT preservation maintained stable viability and functionality after recovery. These findings demonstrate that RT preservation effectively maintains cardiac organoid integrity and function, offering a promising alternative for short-term storage and transport, with potential terrestrial and nonterrestrial applications. Full article

The purpose of side-channel leakage detection is to determine whether or not there is side-channel leakage in the target cryptographic chip. The application of grouping (i.e., dividing the collected power traces into groups based on a property of the intermediate value, such as the Hamming weight of a byte or the bit value of an S-box output) in side-channel leakage detection is a research hotspot. The bit-level grouping mode and the byte-value grouping mode are proposed by previous scholars. However, the bit-level grouping mode does not match the byte operation architecture of cryptographic chips, resulting in an overly fine detection granularity and a high computational complexity. Although the byte-value grouping mode takes into account the byte operation architecture of cryptographic chips, it will cause unequal sizes of traces contained in two groups, reducing the test efficiency. In light of this, we propose the Interval-Optimized Hamming-Weight-Oriented Grouping (IHOG) Mode. IHOG groups data according to the Hamming weight (HW) of byte, dividing them into two groups with Hamming weights of {0, 1, 2, 3} and {5, 6, 7, 8}. In this way, it solves the problem of overly fine detection granularity and high computational complexity caused by bit-level grouping, and it also addresses the issue of unequal sample sizes and low test efficiency caused by the byte-value grouping mode. This paper verifies the effectiveness of the proposed IHOG method using four datasets, namely DPA v4, AES HD, Custom Dataset 1, and Custom Dataset 2. The results show that, compared with three existing grouping schemes such as HW value, bit value, and byte value, the IHOG scheme proposed in this paper increases the accuracy of leakage detection by 37.2%, 18.5%, and 146.3% respectively at the selected leakage points. Full article

The pervasive contamination of aquatic environments by microplastic particles necessitates the development of rapid, cost-effective and field-deployable detection methodologies to complement established but laboratory-bound spectroscopic techniques such as Fourier-transform infrared and Raman microscopy. The demand for field-suitable methods with a broad accessibility comes from researchers themselves. In this review we systematically examine recent advances in optical methods for microplastics identification with a particular emphasis on birefringence as a key diagnostic feature of partially crystalline synthetic polymers. In particular, we analyze three complementary technological directions: liquid crystal-based sensors that exploit orientational order disruptions at interfaces for label-free microplastics detection; polarization holographic imaging combined with machine learning for high-throughput particle classification; and on-chip polarization light microscopy enabling compact and portable analyzing systems. Liquid crystal platforms demonstrate exceptional sensitivity to submicron particles and enable real-time visualization of microplastics aggregation at aqueous interfaces, though they currently lack polymer-specific chemical identification. Conversely, smart polarization holography integrated with Stokes polarimetry and deep learning algorithms achieves over 90% accuracy in distinguishing microplastics from natural particles while processing up to 10,000 particles per minute. Emerging on-chip polarized light microscopy offers a pathway toward miniaturized, low-cost devices suitable for field applications. By synthesizing insights from foundational studies, this review identifies convergent interdisciplinary trends—particularly the integration of artificial intelligence with multimodal optical imaging—and outlines persistent challenges including standardization, interference from natural organic matter, and the transition from laboratory prototypes to robust field-deployable instruments. The systematization of birefringence-based approaches aims to guide future research towards integrated monitoring systems capable of addressing water quality concerns. Full article

As the area occupied by static random access memory (SRAM) continues to increase in advanced integrated circuits, SRAM yield has become a critical factor that directly constrains chip cost and manufacturing efficiency. Conventional SRAM redundancy configuration methods are largely based on ideal random-defect assumptions and therefore cannot accurately characterize the systematic defects that are widely observed in advanced technology nodes. This mismatch often leads to suboptimal redundancy allocation with respect to the actual failure distribution. To address this issue, this paper proposes a data-driven SRAM redundancy optimization method for mass-production applications. The proposed method integrates defect distribution modeling, systematic defect identification, test-algorithm signature extraction, and physical failure analysis (PFA) into a closed-loop framework of test, diagnosis, localization, and optimization. Experimental results based on 7 nm mass-production chips demonstrate that the proposed method can effectively identify systematic defects, achieving an initial PFA localization hit rate close to 100% for single stuck-at faults while significantly improving failure analysis efficiency. Further redundancy evaluation shows that, after the major systematic defects are removed, the required redundancy can be reduced from two-row/two-column redundancy to only single-column redundancy while still covering all repairable failures, thereby improving both area efficiency and manufacturing economy. The proposed method provides a practical engineering solution for SRAM redundancy planning, process tuning, and yield improvement in advanced technology nodes. Full article

Distributed vibration sensing based on optical vector network analysis (OVNA) is a promising technique for measuring dynamic perturbations in optical fibers, but its practical use is limited by the high computational cost of short-time Fourier transform (STFT) and cross-correlation stages. In this work, we present a GPU-accelerated signal processing pipeline, together with an optimization strategy based on dataflow reduction, mixed-precision arithmetic, and hardware-aware tuning. The proposed implementation reduces the processing time for 200 sweeps from 64.7 s on a single-core CPU to 0.199 s on a modern GPU, while preserving the final shift results, with zero mismatches over 199,199 measurement points. Benchmarking across three GPU generations further shows that STFT benefits more from large on-chip cache resources, whereas cross-correlation scales more closely with memory bandwidth. These results suggest that modern GPUs can significantly reduce the computational burden of OVNA, as well as other distributed sensing methods with a similar processing flow, enabling kHz-rate aggregate throughput from batched processing, supporting real-time-oriented operation on modern GPUs. Full article

Background: Parabens are frequently utilized as preservatives in processed foods; nevertheless, the primary dietary factors contributing to exposure in northern Thailand remain undetermined. Methods: A cross-sectional study was conducted among 130 adults in Northern Thailand. Dietary intake was assessed using self-reported food consumption data combined with previously measured paraben concentrations. Due to the skewed distribution of intake, participants were classified into lower and higher exposure groups. LASSO regression was applied for variable selection, followed by multivariable logistic regression to identify dietary predictors of exposure. Results: Several processed food items were significantly associated with higher paraben exposure, including soft drinks, potato chips, and canned fish. No demographic factors were significantly associated with exposure. The final model demonstrated good explanatory power and classification performance. Conclusions: These findings suggest that routine consumption of certain processed foods and beverages may play a larger role in exposure than individual characteristics, and they highlight practical targets, particularly soft drinks, potato chips, and canned fish, for community-based health-promotion strategies aimed at reducing unnecessary preservative intake. Full article

The picking of tea leaves in tea gardens requires multiple batches in the short and valuable tea harvest period. To realize timely and efficient tea plucking, it is feasible to use unmanned aerial vehicles (UAV) for tea shoot detection in large tea gardens. For the typical small targets of tea buds in unmanned aerial vehicle (UAV) aerial images, it is necessary to design an efficient tea buds detection model. In order to improve the accuracy and the speed of the tea buds detection in the UAV images, we designed the SH-CoordMapping hash space mapping algorithm to accelerate the remerging of the detection results into the original image. The C2PSA-BI module and the CARAFE upsampling module are applied to improve detail preservation during feature fusion. A lightweight detection head is further used to reduce redundant computation in the detection stage. By comparing with the traditional detection methods, it can be proved that the SWO sections are necessary for UAV-scale tea shoots detection. Based on the accuracy and the number of model parameters, the YOLO11n model with slice size as 640 and overlap rate as 0.1 performs the best. The TSDet-UAV was deployed on the NVIDIA Jetson Orin NX chip to construct an inspection system capable of real-time acquisition and detection. The experimental results demonstrate that the proposed TSDet-UAV achieves excellent performance, recording an mAP₅₀ of 52.9% on the constructed UAV-TS dataset while maintaining high efficiency. With a parameter size of 2.4 M and a total processing time of 1.32 s per high-resolution image under TensorRT FP16, the processing speed is highly suitable for real-time edge deployment on agricultural UAV platforms. The UAV image-based tea garden shoot inspection platform proposed in this paper can effectively confirm the growth status of tea shoots, assisting farm management in formulating precise picking plans. Full article

Blood disorders encompass a wide range of diseases including anemia, hemophilia, thrombotic disorders, platelet dysfunction, and hematological cancers, making blood disorders a major global health concern. These conditions can impair processes vital to human physiology including oxygenation, coagulation, and immune defense. Hematologic malignancies, both chronic and acute, require timely diagnosis and ongoing disease monitoring for effective clinical management. Microfluidic technologies have emerged as promising alternatives to benchtop techniques for diagnosing and monitoring hematological disorders. For example, microfluidic assays can be used for the isolation and characterization of liquid biopsy markers such as rare cells, extracellular vesicles, and cell-free molecules to support disease management in a minimally invasive manner while the process automation afforded by microfluidics decentralizes healthcare, making it more accessible. Advances in lab-on-a-chip technologies, including large-scale fabrication methods and novel design strategies, will provide tools for the clinical validation of biomarkers and the translation of these technologies from the laboratory bench to the patient bedside. In this review, we will show that microfluidic devices enable disease monitoring via high-throughput analysis of liquid biopsy samples for the detection of rare disease-specific biomarkers found in blood, plasma, urine, etc., providing an alternative to standard benchtop testing using specimens secured via invasive bone marrow procedures, typically used for managing blood-based diseases. A key advantage of microfluidics is their ability to manipulate blood components at scales that closely mimic the body’s microvascular environment. Not surprisingly, microfluidic vascular models have been developed to replicate physiological rheology enabling quantitative assessment of blood cell deformability, aggregation, or clot formation. We provide a critical perspective on the use of the microfluidic “organ-on-chip” designed for blood disorders’ modeling and employed to recapitulate the blood cancer microenvironment. A summary of advances in microfluidic strategies for detection, diagnosis, drug screening, and mechanistic investigations of blood disorders, and future directions for precision testing, will be presented. Full article

Background: Internode length (IL), a key component of plant height (PH), plays an important role in achieving the optimal architecture in wheat. However, the genetic mechanisms underlying internode elongation are not well understood. Methods: In this study, a recombinant inbred line (RIL) population derived from a cross between Bainong 4199 (BN4199) and Zhengyinmai 2 (ZYM2) was evaluated for PH and five ILs across two field locations over two years and genotyped using a 120 K liquid-phase chip. Results: A total of 141 quantitative trait loci (QTL) associated with PH and the five ILs were mapped onto 20 chromosomes, except for chromosome 5D. Among these, 37 stable QTL were identified on chromosomes 1B, 2B, 2D, 4B, 5A, 7A, 7B and 7D, accounting for 3.86–25.97% of the phenotypic variation. Meanwhile, 23 co-localized QTL associated with at least two traits were detected, with QTL cluster regions on chromosomes 2D, 4B, 5A, 7A, and 7B. Moreover, the total additive effects of the QTL combinations increased with the number of QTL, which indicates the effectiveness of pyramid breeding. Additionally, based on gene function annotation, the cloning and characterization of rice orthologs, and analysis via the QTG miner module of the wheat integrative gene regulatory network (wGRN) platform, 63 candidate genes (e.g., Rht1, Rht8, TB1 and ZnF-B) were prioritized within the stable QTL intervals, and their tissue expression patterns were analyzed. Conclusions: Collectively, these findings not only deepen our understanding of the genetic basis of PH and ILs in wheat but also lay a foundation for the further validation and functional characterization of candidate genes, enabling the optimization of plant architecture through marker-assisted selection (MAS) to ultimately improve agronomic performance and yield potential. Full article

This work describes the fabrication and characterization of a novel lateral 4H-SiC PiN diode featuring inter-device isolation technology for power integrated circuits (ICs). The device’s architecture includes thick SiO₂ blocking layers and inter-device trench isolation structures, enabling effective electrical isolation between power devices and control circuitry on a single chip. The devices demonstrate good isolation performance, with inter-device leakage currents below 5 nA at a reverse bias of 200 V. The diodes keep reverse leakage current low (<1 μA at 20 V), but exhibit non-distinct turn-on behavior. This is mostly due to the too-shallow N+ and P+ ion implantation region and high series resistance. A monolithically integrated bridge circuit operating at 50 Hz and 200 Hz validates the integration approach; however, large forward voltage drops show that the high series resistance at the device level affects overall conversion efficiency. The transfer length method (TLM) characterization reveals high sheet and contact resistances, which are responsible for the conduction limitations in the PiN diode forward performance. This study establishes the foundation for a lateral SiC device technology with promising inter-device isolation capabilities, as well as the bridge circuit built based on the lateral PiN diodes, which shows the potential of this technology for future monolithic power IC applications. Full article