Semiconductor manufacturing is becoming increasingly complex due to the continuous scaling of device dimensions, stringent quality requirements, and the demand for high production efficiency. Traditional manufacturing approaches often face challenges related to process variability, equipment failures, defect detection, and yield optimization, limiting their ability to meet the requirements of next-generation integrated circuits. Artificial Intelligence (AI) has emerged as a transformative technology that enables intelligent automation, predictive analytics, and real-time decision-making throughout semiconductor fabrication processes. This paper presents an AI-driven semiconductor manufacturing framework that integrates machine learning, deep learning, computer vision, edge computing, Industrial Internet of Things (IIoT), and digital twin technologies to enhance wafer fabrication, process monitoring, equipment maintenance, and quality inspection. The proposed framework employs deep neural networks for defect classification, reinforcement learning for adaptive process optimization, predictive maintenance models for equipment health monitoring, and digital twins for virtual process simulation and continuous optimization. Furthermore, AI-enabled analytics facilitate anomaly detection, resource allocation, and production scheduling while reducing manufacturing costs and energy consumption. The integration of explainable AI techniques improves transparency and supports informed decision-making in high-precision fabrication environments. The proposed approach is expected to improve manufacturing yield, reduce defect rates, minimize equipment downtime, optimize resource utilization, and enhance overall production efficiency, thereby supporting the development of intelligent, resilient, and sustainable semiconductor manufacturing systems aligned with Industry 4.0 and smart factory initiatives.
Bandla Hansika, Medipelli Aravind, N. Swaroop· International Journal of Sci...· 0 citations
ABSTRACT
Agriculture remains the backbone of many economies worldwide, yet farmers continue to face challenges related to climate variability, water scarcity, soil degradation, pest infestations, and inefficient resource utilization. Traditional farming methods often rely on manual observation and experience-based decision-making, which may lead to reduced productivity and increased operational costs. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has emerged as a transformative solution for modern agriculture by enabling real-time monitoring, intelligent data analysis, and automated decision-making. This paper proposes a Smart Farming System that combines IoT sensors, wireless communication technologies, cloud computing, and AI-based predictive models to optimize agricultural practices. The proposed framework continuously monitors environmental conditions such as soil moisture, temperature, humidity, light intensity, and crop health. AI algorithms analyze the collected data to predict irrigation requirements, detect diseases, estimate crop yield, and recommend suitable farming actions. The system aims to improve agricultural productivity, reduce resource wastage, and promote sustainable farming practices. Experimental results demonstrate significant improvements in water efficiency, crop yield prediction accuracy, and overall farm management performance. The proposed solution provides a scalable and cost-effective approach toward the development of intelligent agriculture systems capable of meeting future food security demands.
Keywords— Smart Farming, Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture, Crop Monitoring, Machine Learning, Sustainable Agriculture, Smart Irrigation.
N. Swaroop, Gudesela Madhu Kumar, D. P. Balaji· International Scientific Jou...· 0 citations
Digital Twin (DT) technology has emerged as a transformative paradigm in the electronics industry by enabling the creation of real-time virtual replicas of physical electronic systems, devices, and manufacturing processes. The integration of Internet of Things (IoT) sensors, artificial intelligence (AI), machine learning (ML), cloud computing, and edge computing facilitates continuous synchronization between physical assets and their digital counterparts, allowing real-time monitoring, predictive analysis, fault diagnosis, and performance optimization. In electronics design and manufacturing, Digital Twins improve production efficiency by detecting defects at early stages, optimizing process parameters, reducing equipment downtime, and enhancing product quality through predictive maintenance and intelligent decision-making. Furthermore, DT technology supports lifecycle management by enabling virtual testing, design validation, thermal analysis, reliability assessment, and energy optimization before physical deployment, thereby minimizing development costs and shortening time-to-market. The incorporation of advanced data analytics and simulation models also enables adaptive manufacturing, supply chain optimization, and sustainable electronics production. Despite these advantages, several challenges remain, including high computational requirements, interoperability among heterogeneous systems, cybersecurity risks, data privacy concerns, and the need for standardized communication frameworks. This paper presents a comprehensive overview of Digital Twin technology in electronics, discussing its architecture, enabling technologies, applications, benefits, and current research challenges. The study also highlights future research directions involving AI-driven autonomous Digital Twins, federated learning, blockchain-enabled secure data sharing, explainable artificial intelligence, and next-generation intelligent electronic systems for Industry 5.0. The findings demonstrate that Digital Twin technology has significant potential to revolutionize electronic system design, manufacturing, maintenance, and lifecycle management through intelligent, data-driven, and autonomous operations.
Malloju Dushyanthachary, Edla Chandu, N. Swaroop· International Journal of Sci...· 0 citations
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