The Impact of Chip Development on Engineering Fields: Innovations in Integrated Circuits and Semiconductor Technologies

The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in organizational digital transformation strategies. This research investigates the revolutionary impact of AI/ML integration within SAP ecosystems, examining performance improvements, operational efficiency gains, and strategic advantages. Through comprehensive analysis of implementation patterns across diverse industry sectors, this study presents empirical evidence demonstrating significant enhancements in predictive analytics, automated decision-making, and resource optimization. The findings reveal that organizations implementing AI-enabled SAP solutions experience an average 35% improvement in operational efficiency, 42% reduction in manual processing time, and 28% increase in predictive accuracy for demand forecasting. This research contributes to the growing body of knowledge on intelligent ERP systems and provides practical insights for enterprise digital transformation initiatives.

With the development of integrated circuit (IC) chips, many elongated chips with small amounts of textures have begun to be used in IC packaging and manufacturing. Traditional template-matching methods are not effective for positioning these chips; this limitation results in IC equipment that cannot accurately pick up an elongated IC chip or may package a nonworking IC chip onto an electronic device. In this paper, a novel region-based normalized cross correlation algorithm that uses a signal function to separate the influences of the IC chip and the background pixel information is proposed. To obtain an increased speed, the bounding box of the IC chip is employed to rapidly estimate the region of interest of an IC chip in a target image. Various experiments using LED sorting equipment demonstrate that our algorithm results in correct positioning for elongated IC chips and can distinguish good chips from nonworking chips with reasonable accuracy. When the size of the target image is 512 × 512 pixels and the size of the template image is smaller than 140 × 140 pixels, the matching time of our algorithm is less than 14 ms. This algorithm may have many applications in the field of IC packaging.

This article addresses the growing concern of security vulnerabilities in hardware systems, particularly in the context of integrated circuit (IC) chips used in cryptographic applications, which are increasingly susceptible to adversarial physical attacks in real-world environments. These attacks, often targeting the physical integrity of ICs, exploit weaknesses in both the design and manufacturing processes of cryptographic circuits. The paper provides a comprehensive overview of these threats, focusing on various physical attack methods, such as sidechannel attacks, electromagnetic (EM) attacks, and laser fault injection attacks, which compromise the confidentiality and functionality of secure systems. It also highlights the vulnerabilities inherent in IC chips, particularly in relation to how attackers can manipulate or exploit system weaknesses during operation. The article delves into the protection mechanisms that are being integrated into modern cryptographic IC chips to prevent or mitigate these physical threats. One of the key strategies discussed is the use of on-chip monitoring circuits, which can actively sense and respond to adversarial attempts, providing an early warning or immediate countermeasures. These monitoring circuits are designed to detect unusual patterns that could indicate an ongoing attack, such as fluctuations in power or electromagnetic emissions, enabling dynamic protection of the system during operation. Additionally, the paper explores innovative physical structures, such as backside buried metal (BBM) wirings in silicon (Si) substrates, which are combined with frontside complementary metal–oxide semiconductor (CMOS) circuits to create robust defenses against a variety of physical attacks. This integration of backside metal wirings with frontside CMOS technology is a novel approach that enhances the IC's resilience by providing a physical barrier that reduces the ability of attackers to exploit weaknesses in the power delivery network and other sensitive components. By creating a unified approach to IC design and packaging, the paper emphasizes the importance of incorporating multiple layers of protection, such as detection, avoidance, and resilience, against electromagnetic and laser-based attacks. The combination of these advanced technologies establishes a more secure architecture that can defend against multimodal attacks, thus ensuring the integrity of cryptographic systems. The article also highlights the practical implementation of these protective strategies, including tests with silicon demonstrators to validate the effectiveness of the proposed solutions. The results show promising outcomes, demonstrating the feasibility of creating secure hardware systems that can withstand physical attacks and provide reliable protection for sensitive data and cryptographic operations. Through the integration of on-chip monitoring, advanced IC design principles, and packaging innovations, this paper offers a roadmap for developing secure, resilient hardware systems capable of withstanding the evolving threat landscape posed by physical attacks on IC chips. Ultimately, the proposed protection schemes not only strengthen the security of cryptographic circuits but also contribute to the broader field of hardware security, providing insights for future designs and applications.

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a transformative strategy to enhance efficiency, resilience, and adaptability in complex global logistics networks. This proposes a comprehensive model for AI integration in supply chain optimization, drawing comparative insights from the United States and Nigeria two economies with contrasting technological infrastructures, regulatory frameworks, and economic dynamics. The model emphasizes a multi-tiered approach incorporating predictive analytics, real-time decision-making, and intelligent automation. In the U.S., AI adoption is characterized by advanced infrastructure, data availability, and investment in digital transformation, enabling real-time inventory management, demand forecasting, and autonomous logistics. Conversely, Nigeria presents a context of infrastructural challenges and data limitations, yet exhibits increasing potential for AI-driven solutions through mobile technology, cloud computing, and emerging policy support. The proposed model comprises four core pillars: (1) AI-readiness assessment focusing on digital infrastructure and data maturity; (2) phased AI adoption strategy tailored to sector-specific needs; (3) stakeholder collaboration for technological transfer and capacity building; and (4) policy alignment to foster ethical AI use and cross-border interoperability. Through comparative analysis, the model identifies key enablers and barriers within each country, highlighting opportunities for technology leapfrogging in Nigeria and continuous innovation in the U.S. This argues that while the scale and mode of AI integration may differ, the strategic alignment of technology, talent, and governance is essential for effective supply chain optimization in both regions. This research contributes to the discourse on inclusive AI deployment by illustrating how adaptable frameworks can bridge technological disparities and drive supply chain resilience. Ultimately, it underscores the importance of localized strategies within a global model, fostering both economic growth and digital equity. The findings serve as a guide for policymakers, industry leaders, and technology developers seeking to harmonize AI integration across diverse socio-economic contexts.

On the use of new generation mobile phone (smart phone) for retrospective accident dosimetry

Integration of artificial intelligence and machine learning technologies in antiviral drug discovery: strategies and challenges

The integration of Artificial Intelligence and Machine Learning technologies into Computer-Aided Design systems represents a transformative approach to addressing longstanding challenges in engineering design processes. Traditional metadata-based search methods have proven inadequate for efficiently locating existing components, resulting in significant economic losses across manufacturing sectors. Shape-based search emerges as a compelling alternative, leveraging advanced deep learning architectures to enable intuitive geometric similarity matching. This capability fundamentally alters how engineers interact with design repositories, allowing for component retrieval based on visual similarity rather than textual descriptions. The implementation of shape-based search yields substantial benefits, including dramatic reductions in search time, increased component reuse rates, and enhanced design standardization. While integration challenges exist, organizations successfully deploying these technologies report compelling return on investment through reduced development cycles and lower certification costs. As computational technologies continue to advance, the application of geometric deep learning to CAD search promises to further revolutionize engineering knowledge management by enabling cross-domain component discovery and function-based retrieval capabilities.

TrustAIoT: A framework for building trustworthy AIoT platforms

Introduction: The digital technology in healthcare has revolutionized during the last decade with the increased implementation of Electronic Health Record (EHR) systems. The growth of EHR has been from the simple database system to incorporating the applications of artificial intelligence and machine learning systems in the system. One of the most important aspects of digital system proves to be electronic health record system because of the intense data produced geographically which can be used for epidemiological studies and research. The EHRs also have a wide range of benefits such as effective communication between the clinicians, clinicians and laboratory, decreased human errors, effective tool for diagnosis and treatment planning, increased patient satisfaction, and quality care. Materials and Methods: The quantitative study included opinions and perspectives of dental clinicians toward EHR system. The data collection of the participants was from April to December 2024. The quantitative part involved a descriptive survey. After obtaining written informed consent from each participant, a structured survey e-questionnaire was given to dental clinicians to fill it through online mode by dental practitioners who had independent clinics. The sample size calculation done by www.openepi.com software quantitative study which included two hundred and eighty five (285) dentists participated. Results: The findings indicated a favorable disposition among dentists regarding the adoption of EHR; however, dental practitioners also emphasized the problems encountered during EHR implementation. Conclusion: EHRs are an effective way inside health information systems; however, they necessitate ongoing training, regular upgrades, and the integration of artificial intelligence and machine learning technologies in the future. The threat to data security continues to be a concern for dental practitioners due to the anticipated rise in cybercrimes, as patient-sensitive information is highly confidential.

n dynamic and changing conditions of enterprise functioning, there is an objective necessity and constant need to respond to external environmental factors, since they set the course of market develop-ment, determine demand and new needs, which makes it advisable to form such a management system that will ensure potential development. In such conditions, studying the state and trends of enterprise development, influencing factors, cause-and-effect relationships, and market performance results allows us to research and form a theoretical-applied, information-analytical, practical-methodological and fore-casting basis for establishing priority strategic directions of enterprise development. The objectives of the article are to study, form and substantiate the factors influencing organiza-tional changes in the context of globalization according to typological features. To construct a typology of factors influencing organizational changes in the context of globalization. To describe them. To study the impact of globalization on organizational changes. To study the features of organizational change man-agement in the context of globalization processes, as well as to identify the main trends in the development of modern enterprise resource planning systems. The given typology of organizational changes is rather conditional. It can be expanded by taking into account additional typological features. According to the urgency of the changes, urgent (urgent) and non-urgent (usual) changes can be distinguished. Urgent changes require immediate action, for example, as a result of a malfunction of the means of production, equipment requires its urgent replacement. As for non-urgent changes, they do not provide for their immediate implementation. According to the depth of impact, we distinguish minor and cardinal changes. Cardinal changes include: aging of products, tech-nologies; decrease in labor productivity and product quality; change in the goals and objectives of the organization. According to the pace of changes, rapid, slow, increasing, uniform and wave-like changes are distinguished. According to the range of changes, we distinguish macroeconomic and microeconomic. Macroeconomic changes include changes of an external large-scale (national economy level and global nature) nature, these can be legislative, economic, political, scientific and technological, international events, etc. Microeconomic changes include minor changes in the organization, for example, replacement of office equipment for employees, etc. According to quantitative, qualitative and structural characteris-tics - quantitative, qualitative, structural. According to the depth of changes, basic, improvement, modifi-cation are distinguished. According to the term of implementation of changes - those that provide for a change lag; those that do not provide for a change lag. According to tasks - current changes, prospective, tactical, operational and operational changes. According to the nature of the implementation of changes - active changes, gradual changes. According to the speed of changes - one-time, short-term, medium-term, long-term. The article investigates, formulates and substantiates the factors influencing organizational changes in the context of globalization. A typology of factors influencing organizational changes in the context of globalization is constructed. Their characteristics are given. The impact of globalization on organizational changes is investigated. The features of organizational change management in the context of globalization processes are given. It is found that in the context of globalization, most enterprises prefer to use various software related to the enterprise resource planning system ERP, customer relationship management CRM, project and program management tools, e-commerce platform, data visualization tools, etc., and the main trends in the development of modern enterprise resource planning systems are the transition to a cloud environment, the integration of artificial intelligence and machine learning tech-nologies, providing mobile access to ERP systems, the use of convenient interfaces.

Purpose This paper aims to develop the thermal resistance network model based on the heat dissipation paths from the multi-die stack to the ambient and takes into account the composite effects of the thermal spreading resistance and one-dimensional (1D) thermal resistance. The thermal spreading resistance comprises majority of the thermal resistance when heat flows in the horizontal direction of a large plate. The present study investigates the role of determining the temperature increase compared to the thermal resistances intrinsic to the 3D technology, including the thermal resistances of bonding layers and through silicon vias (TSVs). Design/methodology/approach This paper presents an effective method that can be applied to predict the thermal failure of the heat source of silicon chips. An analytical model of the 3D integrated circuit (IC) package, including the full structure, is developed to estimate the temperature of stacked chips. Two fundamental theories are used in this paper – Laplace’s equation and the thermal resistance network – to calculate 1D thermal resistance and thermal spreading resistance on the 3D IC package. Findings This paper provides a comprehensive model of the 3D IC package, thus improving the existing analytical approach for predicting the temperature of the heat source on the chip for the 3D IC package. Research limitations/implications Based on the aforementioned shortcomings, the present study aims to determine if the use of an analytical resistance model would improve the handling of a temperature increase on the silicon chips in a 3D IC package. To achieve this aim, a simple rectangular plate is utilized to analyze the temperature of the heat source when applying the heat flux on the area of the heat source. Next, the analytical model of a pure plate is applied to the 3D IC package, and the temperature increase is analyzed and discussed. Practical implications The main contribution of this paper is the use of a simple concept and a theoretical resistance network model to improve the current understanding of thermal failure by redesigning the parameters or materials of a printed circuit board. Social implications In this paper, an analytical model of a 3D IC package was proposed based on the calculation of the thermal resistance and the analysis of the network model. Originality/value The aim of this work was to estimate the mean temperature of the silicon chips and understand the heat convection paths in the 3D IC package. The results reveal these phenomena of the complete structure, including TSV and bump, and highlight the different thermal conductivities of the materials used in creating the 3D IC packages.

Design and evaluation of microfluidic devices for two-dimensional spatial separations