High Integration Research Articles

Modeling and experimental characterization of a compact Mach–Zehnder electro-optic modulator on the SOI

The Mach–Zehnder electro-optic modulator (MZM) plays a crucial role in photonics integration technology during signal transmission. We propose the design and fabrication of a silicon-based electro-optic modulator based on the M-Z structure and design a modulator using silicon as the waveguide core layer on a silicon dioxide substrate. The detailed design involves a 1×2 splitter, branch waveguides, modulating arm waveguides, and a 2×1 combiner. The MZM fabrication and characterization results reveal that the half-wave voltage of the silicon-based MZM is 2 V, with an optical loss of −2.464dB and a device core size of 450µm×2800µm. The experimental results indicate that the MZM with a low half-wave voltage, a low loss, and high integration has significant value. The proposed MZM exhibits considerable improvements, featuring a low half-wave voltage and a low loss, and this device may serve as a fundamental component in wearable large-scale photonic integrated circuits for weak ECG real-time detection.

AI-Driven Quality Control in PCB Manufacturing: Enhancing Production Efficiency and Precision

This paper investigates the application of Artificial Intelligence (AI) in enhancing quality control within Printed Circuit Board (PCB) manufacturing processes, focusing on how AI-driven technologies improve production efficiency and precision. The research addresses traditional quality control methods, such as manual inspection and Automated Optical Inspection (AOI), highlighting their limitations in keeping up with the complexities and demand for high-precision PCBs in modern electronics. The study delves into how AI technologies, particularly machine learning, computer vision, and predictive analytics, are being leveraged to overcome these limitations. By automating defect detection, improving accuracy, and enabling real-time analysis, AI systems not only streamline the quality control process but also significantly reduce human error and production costs. AI-driven quality control systems are shown to increase defect detection rates, reduce inspection times, and enhance overall production throughput. The paper includes a comparative analysis between traditional and AI-based quality control methods, revealing a notable improvement in both detection accuracy and production speed when AI systems are employed. Additionally, cost efficiency is explored, demonstrating how AI systems reduce waste, minimize rework, and lower operational costs. The potential challenges of AI implementation, such as the high initial costs, data requirements, and integration with legacy systems, are also discussed. The paper concludes that despite these challenges, AI offers a transformative solution to the growing demands of the PCB manufacturing industry, with the ability to scale effectively and adapt to future technological advancements. Ultimately, this research underscores the significant role of AI in revolutionizing PCB quality control by providing a more efficient, precise, and cost-effective approach, paving the way for further innovation in the electronics manufacturing sector.

The Role of Digital Technologies in Climate Change Management: Strategies for Minimizing the Environmental Impact of USA Companies

The article examines the role of digital technologies in climate change management and reducing the environmental impact of U.S. companies. Innovations such as artificial intelligence, the Internet of Things, and big data are analyzed for their ability to help companies minimize CO2 emissions, optimize resource use, and improve energy efficiency. Challenges companies face when implementing digital solutions: high initial costs, integration difficulties, and regulatory complexities, are discussed. Strategies for overcoming these barriers to achieve sustainable development are proposed.

Systolic array-based CNN accelerator soft error approximate fault tolerance design

To satisfy the massive computational requirement of Convolutional Neural Networks, various Domain-Specific Architecture based accelerators have been deployed in large-scale systems. While improving the performance significantly, the high integration of the accelerator makes it much more susceptible to soft-error, which will be propagated and amplified layer by layer during the execution of CNN, finally disturbing the decision of CNN and leading to catastrophic consequences. CNNs have been increasingly deployed in security-critical areas, requiring more attention to reliable execution. Although the classical fault-tolerant approaches are error-effective, the performance/energy overheads introduced are non-negligible, which is the opposite of CNN accelerator design philosophy. In this article, we leverage CNN's intrinsic tolerance for minor errors and the similarity of filters within a layer to explore the Approximate Fault Tolerance opportunities for CNN accelerator fault tolerance overhead reduction. By gathering the filters into several check groups by clustering to perform an inexact check while ensuring that serious errors are mitigated, our approximate fault tolerance design can reduce fault tolerance overhead significantly. Furthermore, we remap the filters to match the checking process and the dataflow of systolic array, which can satisfy the real-time checking demands of CNN. Experimental results exhibit that our approach can reduce 73.39%performance degradation of baseline DMR.

High precision DSRC and LiDAR data integration positioning method for autonomous vehicles based on CNN

In order to improve the safe driving and automatic positioning capability of autonomous vehicles, a high-precision DSRC and LiDAR data integration positioning technology for autonomous vehicles based on CNN is proposed. Import the data of Dedicated Short Range Communications and Light Detection and Ranging for automatic driving vehicle positioning, carry out kinematic analysis of autonomous driving vehicles under multi-sensor fusion, and transform the data of DSRC and LiDAR sensors into tightly coupled coordinate systems; The CNN depth learning method is used to compensate the position and attitude tracking estimation error under the overall time stamp synchronization of the sensor through adaptive information tracking; The first and second order feedforward compensation is made for the positioning parameters of the autonomous driving vehicle using the PID model, and the point cloud feature matching model is fused to complete the estimation of the positioning attitude parameters of the autonomous driving vehicle. In order to eliminate the noise interference under the DSRC communication mechanism, the Kalman filter function is used to automatically optimize the constraint parameters in the point cloud feature detection model, and the positioning error parameters are dynamically filtered and adjusted; Kinematics analysis is carried out for the driving state of the vehicle, and the positioning error in the vehicle movement is controlled through the difference technology to achieve high-precision DSRC and LiDAR data integration positioning. The simulation results show that this method can integrate and locate the high-precision DSRC and LiDAR data of the autonomous vehicle, and the attitude estimation and positioning accuracy of the vehicle is good, while the error of the attitude parameter estimation of the autonomous vehicle is low.

Development of a Versatile and Reversible Multi-Stack Solid Oxide Cell System towards Operation Strategies Optimization

Abstract In context of Temperature Electrolysis based on Solid Oxide Cell technology, CEA LITEN has developed a new investigation tool (MURPHY) devoted to the operation of several Solid Oxide stacks. MURPHY enables stack operation in both the electrolysis (SOE) and the fuel cell (SOFC) modes. For the later, H2, CH4, and natural gas can be used as fuel. A compact Balance of Plant (BOP) is designed to (i) provide air by centrifugal blower, (ii) target high thermal integration and performances, (iii) preheat inlet gases, (iv) recycle fuel exhaust, and (v) control pressure levels. Instrumentation was defined to support modeling development and accurate process efficiencies characterization. 
MURPHY is currently compatible with four stacks of CEA standard base design (25 cells, of 100 cm² active area), the corresponding maximum power range of the module is -16/4 kWDC for supply and venting of gases produced.
This report details system architecture. It also puts forward experimental results related in an environment controlled by thermal phenomena. Performance and efficiency curves obtained under parametric variations of temperature and flowrates are reported for both SOE and SOFC-H2 modes. A special attention is given to heat performance. In this view, flow parameters at several locations over circuitries are provide

Simulated and experimental study of the chip deformation mechanisms of monocrystalline Cu

Monocrystalline Cu exhibits excellent electrical and signal-transmission properties due to its absence of grain boundaries, making it a critical material for the production of micro-machinery and micro-components; however, achieving ultrahigh precision and ultralow damage machining of functional devices using traditional techniques such as grinding and polishing is extremely challenging. Consequently, nanocutting has emerged as an efficient means to fabricate monocrystalline materials with complex surface characteristics and high surface integrity. Nevertheless, the macroscopic cutting theory of metal materials cannot be applied to nanocutting. Accordingly, in this paper, both simulations and experiments were conducted to examine the chip deformation mechanisms of monocrystalline Cu. First, large-scale molecular dynamics (MD) simulations were conducted to gain a comprehensive understanding of the deformation behavior during nanocutting. This included examining the influencing factors and the variation patterns of the chip deformation coefficient, cutting force, and minimum cutting thickness. Subsequently, nanocutting experiments were performed using a specially designed nanocutting platform with high-resolution online observation by scanning electron microscopy. The experimental results served to verify the accuracy and reliability of the MD modeling, as they exhibited excellent consistency with the simulated results. Although this work considered monocrystalline Cu, it is believed that the elucidated chip deformation mechanisms could also be applied to other face-centered-cubic metals. These results are of great value for advancing the understanding of the mechanisms of ultraprecision cutting.

Video SAR Moving Target Detection Method Based on Machine Vision

When detecting moving targets in video SAR, it is impossible to accurately obtain the moving targets in dynamic images. In order to solve the problems of low detection integrity and poor effectiveness, a video SAR moving target detection method based on machine vision is proposed. Firstly, the moving target pixel points are matched by the mixed Gaussian model to obtain the target area of the video SAR moving image; secondly, the HSV model and reflectivity algorithm are used to remove the shadows caused by the moving target; finally, the processed target area is input into the YOLO algorithm to complete the final video SAR moving target detection. Experimental results show that the proposed algorithm has high detection integrity, high detection rate, low false detection rate, and better detection effectiveness.

Historic street layout and its impact on shaping residents’ daily lives: insights from Sanxue Street, Xi’an

ABSTRACT Historic street layouts are integral to historic districts’ tangible cultural heritage and are intricately linked to the residents’ daily lives; however, existing studies have only examined their spatial aspects, primarily using quantitative methods. Conversely, studies on tangible cultural heritage’s impact on residents’ lives have principally employed qualitative methods. Therefore, this study bridges qualitative and quantitative approaches to propose a layout – daily life model, which explains the relationship between the layout of Xi’an, China’s Sanxue Street historic and cultural district, and its residents’ daily lives. A combination of space syntax and geographic information system-based land-use analyses revealed that eating, shopping, calligraphy, and transportation were the main activities of residents. Questionnaire responses from 335 participants validated the proposed model and confirmed its feasibility. When certain types of facilities are widely distributed in streets with high choice and integration, residents’ daily lives tend to involve the functions represented by them. Furthermore, calligraphy is an important part of most residents’ daily lives, regardless of the transportation mode used. This study contributes to the understanding of the effect of tangible cultural heritage, particularly street layouts, on residents’ daily lives in historic districts.

Printed Interconnects for Heterogeneous Systems Integration on Flexible Substrates

AbstractHeterogeneous integration on flexible substrates has been explored in recent years to meet the high‐performance and flexible form factors requirements of several emerging applications. Reliable interconnects, in both 2D and 3D layouts, are critical for the effective use of these systems. But it is challenging to employ conventional bonding and interconnect technology due to considerable mismatches in mechanical and thermal properties. For example, the ultra‐thin chips (UTCs) are too fragile to withstand the static or oscillating forces applied during conventional bonding. In this regard, printing of metal tracks on flexible substrates is a promising alternative to access the contact pads on integrated circuits (ICs), as the interconnects on diverse substrates can be realized at room temperature processing using as much materials as needed. Further, it is easier to attain step coverage. Considering the above attractive features, and the challenges associated with conventional bonding techniques, this article focuses on the development of high‐resolution printing interconnects in both 2D and 3D layouts and explores various printing routes available for high density integration. The article also discusses major conventional interposers/interconnects forming methods and their limitations for flexible hybrid electronics along with few examples of printed interconnects to highlight the opportunities for future.

Does Hospital–Physician Integration Improve Hospital Performance? Results from a USA Longitudinal Study

In a dynamic healthcare industry, aligning the goals and objectives of hospitals and physicians through integration has been suggested to influence performance. Physicians’ leadership and active involvement in governance can direct resource usage, Electronic Health Record (EHR) implementation, price negotiation, better coordination, and continuity of services for patients, thus affecting performance. This study aimed to examine the relationship between physician integration and hospital performance, investigating both financial and quality outcomes. We used a longitudinal study design. Our sample was hospital-level data from 2014 to 2019, which contained 6000 U.S. hospital-year observations. The dependent variables were quality outcomes (readmission rates) and financial outcomes (total and operating margins). The independent variable explored three dimensions of integration: high, low, and overall integration. Findings showed no impact of hospital–physician integration on quality outcomes and financial performance. High-integration hospitals did not show any significant relationships with quality outcomes and financial performance compared to hospitals that did not have high integration. Hospital–physician integration may have little potential to bring clinical integration even though vertical integration is present. A commitment to improving quality as a strategic priority may be vital in impacting quality outcomes, followed by financial performance.

Profiles of cardiometabolic risk and acculturation indicators among South Asians in the US: latent class analysis of the MASALA study.

South Asians (SA) represent the fastest growing US immigrant group, and previous studies have indicated that they face disproportionately high burden of cardiometabolic disease. Cardiometabolic disease manifests as a syndemic or synergistic epidemic encompassing multiple disease clusters influenced by biological, social, and psychological factors stemming from the acculturative process. This process may exacerbate morbidity within immigrant subgroups. Our aim was to identify cardiometabolic risk profiles among SA using indicators of acculturation. We conducted a latent class analysis on data from the Mediators of Atherosclerosis in South Asians Living in America study (N=771). A composite cardiometabolic disease outcome was constructed using prevalent hypertension, type 2 diabetes, and body mass index. Acculturation indicators included years living in the US, English language proficiency, dietary behaviors, preservation of cultural traditions, social and neighborhood support, maintenance of social relationships (i.e., friendships), and experiences of discrimination, along with proxies of acculturative stress (i.e., depressive symptomology, trait anxiety and anger). Social and environmental determinants of health, health behaviors, religiosity and spirituality served as covariates to further assess latent class membership. Four cardiometabolic risk profiles emerged: (1) lowest risk [73.8% of sample] characterized by high integration into both SA and US cultures; (2) the modest risk [13.4% of sample], exhibiting elevated levels of mental health distress and experiences of discrimination, and distancing themselves from both cultures; and the (3) moderate risk [8.9% of sample] and (4) highest risk [3.9% of sample], demonstrating greater assimilation into US culture. Compared to the lowest risk profile: the modest risk profile was associated with low-income and conflicting attitudes about religion/spirituality, while the moderate risk profile was characterized by lower income and educational attainment with positive behaviors and attitudes toward religion/spirituality. Findings expand our understanding of immigrant cardiometabolic health as a syndemic issue wherein multiple co-occurring and interacting processes synergize to produce negative outcomes in already at-risk subpopulations. Furthermore, acculturation emerges as a crucial factor in understanding health disparities among immigrant and refugee groups in the US.

Acculturation Orientations Among Immigrant-Origin Youth: How is Acculturation Associated with Self-Esteem, Sense of Belonging, and Discrimination?

AbstractWe developed a six-item Compact Acculturation Scale (CAS) based on common items on comprehensive acculturation scales and examined it with two designs using data from the Finnish Annual Youth Future Report Survey of 2023 (N = 744 immigrant-origin youth, IOY). In Study 1, we first confirmed second-order factorial structure of CAS and its convergent validity. Second, using structural equation modelling, we found that the relation of acculturation with self-esteem was ambivalent among recently migrated youth who perceived discrimination. Established immigrant-origin youth who reported discrimination were more likely to prefer an ethnic orientation. In Study 2, we examined acculturation profiles among immigrant-origin youth based on CAS variables, and the relationship of profiles with self-esteem, perceived discrimination, and sense of belonging. Six profiles were found: Medium integration (49%), High integration (22%), Neo-culture kid (15%), Separation (6%), Assimilation (6%), and Marginalized (2%). Both integration profiles were associated with high self-esteem and sense of belonging. Third largest profile was characterized by rejecting both host and heritage cultures, but unlike Marginalized, they reported enjoying both in-group and host-peer relations. In sum, this study shows that CAS is a reliable and robust instrument that can serve us in investigating acculturation both by variable- and person-oriented approaches.

A novel water‐based mechanochemical approach for surface modification of graphene platelets and their epoxy nanocomposites

AbstractGraphene (nano) platelets (GPs) are cost‐effective and possess high structural integrity, but their limited surface functional groups hinder their compatibility with polymers. In this study, an eco‐friendly, water‐based mechanochemical approach was developed to modify GPs with polyacrylamide (PAM), creating PAM graphene platelets (PAM‐GPs) with a grafting ratio of approximately 15%. The platelet was measured to be a few nanometers in thickness, and the grafted PAM provides abundant functional groups, including amide groups, which enhance compatibility with polymers. A typical polymer, bisphenol‐A epoxy, was compounded with PAM‐GPs, resulting in a high degree of exfoliation and dispersion of PAM‐GPs within the matrix. The resulting epoxy/PAM‐GP composites exhibited significantly improved mechanical properties, including an 18% increase in Young's modulus and a substantial enhancement in fracture toughness, with a 71% increase at a low filler fraction of 0.5 wt%. These improvements are attributed to the effective interfacial interaction between the PAM‐GPs and the epoxy matrix, facilitated by the grafted functional groups. This study highlights the potential of PAM‐GPs as toughening fillers for epoxy composites, emphasizing their applicability in enhancing the durability and performance of structural components in industrial applications.Highlights Polyacrylamide (PAM)‐modified graphene platelets by a water‐based approach. A strong interface promoted exfoliation and dispersion of platelets. PAM‐modified platelets improved epoxy modulus and toughness. Fractographic analysis unveils toughening mechanisms.

Modeling and simulation study of acoustic response for dual-membrane capacitive MEMS microphone

Abstract As the micro-electro-mechanical systems (MEMS) technology matures, MEMS sensors have found widespread applications in mechatronics, robotics, and voice control. The high stability, high integration, and radio frequency interference resistance of MEMS microphones have rapidly led to their adoption in these domains, displacing electret condenser microphones.This article focuses on modeling and analyzing dual-membrane capacitive MEMS microphone, utilizing an lumped equivalent circuit model to calculate the microphone’s acoustic frequency response. Furthermore, the article employs the finite element method (FEM) to conduct a detailed analysis of the resonant frequency of the membrane, as well as to explore the effects of changes in the volume of the front and back chamber on the packaging performance of MEMS microphones. Finally, physical tests were conducted, and the simulation results of the two models showed considerable consistency with the curves obtained from the physical tests, verifying the accuracy of the models.

Design of an FPGA-based short-circuit fault diagnosis algorithm for DC current-limiting fuses

Abstract In recent years, DC current-limiting fuses have played a crucial role in the comprehensive power systems of ships, rail transport, and new energy sectors, ensuring the safe and reliable operation of DC power systems. However, with the increase in the capacity of DC power systems, short-circuit faults can lead to a rise in short-circuit current at rates of up to 40 A/μs, with peak short-circuit currents reaching up to 105KA. This poses higher demands on the accuracy and speed of fuse diagnostics to interrupt short-circuit currents. To address this issue, this paper introduces a fault diagnostic algorithm based on FPGA that can calculate the rise rate of short-circuit currents in real-time: employing FPGA with its high integration, abundant logic resources, and parallel data processing capabilities as the core of the measurement and control system to enhance the speed of short-circuit fault diagnosis from a hardware perspective; replacing the common large current trip protection and DDL protection algorithms with the Newton interpolation algorithm. This improvement calculates the rise rate of each current data point instead of the average rise rate over a period, thus enhancing the speed and accuracy of short-circuit fault diagnosis from a software perspective. The paper also discusses the design of the system’s hardware and software and the construction of the experimental platform. Simulation and experimental results show that the fault diagnosis time of the algorithm has been reduced from 98μs to 36μs, with the numerical error in the short-circuit current rise rate within 2%, and a significant reduction in the peak value of short-circuit current, meeting performance requirements and effectively ensuring the safe and reliable operation of DC power systems.

Wafer-Scale Atomic Layer-Deposited TeOx/Te Heterostructure P-Type Thin-Film Transistors.

There is an increasing demand for p-type semiconductors with scalable growth, excellent device performance, and back-end-of-line (BEOL) compatibility. Recently, tellurium (Te) has emerged as a promising candidate due to its appealing electrical properties and potential low-temperature production. So far, nearly all of the scalable production and integration of Te with complementary metal oxide semiconductor (CMOS) technology have been based on physical vapor deposition. Here we demonstrate wafer-scale atomic layer-deposited (ALD) TeOx/Te heterostructure thin-film transistors with high uniformity and integration compatibility. The wafer-scale uniformity of the film is evidenced by spatial Raman mappings and statistical electrical analysis. Furthermore, surface accumulation-induced good ohmic contact has been observed and explained by the unique band alignment of the charge neutrality level inside the Te valence band. These results demonstrate ALD TeOx/Te as a promising p-type semiconductor for monolithic three-dimensional integration in BEOL CMOS applications incorporated with well-established n-type ALD oxide semiconductors.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Towards electrochemical machining of powder superalloy René 88DT with high surface integrity in different solvent-based electrolytes

Electrochemical machining (ECM) presents numerous applications in the production of aero-engine components. The effects of current density on surface quality during ECM of René 88DT in NaCl-ethylene glycol and NaNO3-aqueous solutions was investigated here. The results indicated that the René 88DT surface has lower sensitivity to current density and more homogeneous electrochemical dissolution in NaCl-glycol solution than in NaNO3-aqueous solution, which results in the suppression of stray corrosion and consistently high surface quality regardless of current density. These strengths are attributed that C2H5O2- and Cl- ions replace OH– ions and combine with alloy ions to form solubles and complexes, which prevent the formation of obstructive passive film, oxide layer, and insoluble electrolytic products. The electrochemical dissolution model in two solutions was established. Furthermore, the high tensile strength and precision machinability of René 88DT in NaCl-glycol solution were also demonstrated.

Exploring the impact of AI-driven and blockchain-enabled tax filing systems on smes in the era of technological innovation: A review of benefits, challenges, and adoption barriers

The emergence of blockchain technology and artificial intelligence (AI) presents transformative opportunities for tax compliance, especially for small and medium-sized enterprises (SMEs). These technologies promise to enhance tax system accuracy, efficiency, and transparency. However, their adoption is accompanied by significant challenges and barriers. This review analyzes the benefits, challenges, and future prospects of integrating AI and blockchain technologies into tax systems, with a focus on their impact on SMEs. It seeks to provide insights into how these technologies can reshape tax compliance and identify areas requiring further research. The review revealed that AI enhances accuracy and compliance through advanced predictive models and explainable AI, while blockchain ensures transparency and trust with its immutable ledger and smart contracts. Despite these benefits, SMEs face technical and financial constraints, such as high implementation costs and integration complexities. Regulatory and legal challenges, including evolving tax laws and data privacy requirements, further complicate adoption. Additionally, resistance to new technologies and skill gaps within SMEs hinder widespread implementation. Scalability and customization issues also pose significant barriers. In conclusion, AI and blockchain technologies substantially improve tax compliance but come with notable challenges. These technologies promise enhanced accuracy and efficiency for SMEs, yet overcoming barriers related to cost, integration, and regulation is essential for successful adoption. It was recommended that future research should focus on developing SME-specific solutions, addressing regulatory adaptations, and conducting comparative analyses of traditional versus AI-driven tax systems. By tackling these areas, stakeholders can better support SMEs in leveraging AI and blockchain technologies for more effective and transparent tax compliance.