Authors: Saad, A. M. H.
Journal: Next Research
This research explores the integration of artificial intelligence (AI) in the design and discovery of nanomaterials, particularly for energy-efficient semiconductor devices. It emphasizes AI-enabled high-throughput screening, machine learning-based inverse design, and multiscale simulations to accelerate the identification of nanomaterials with desired properties for electronic applications like transistors, solar cells, and thermoelectric materials. The research hypothesizes that AI can significantly enhance semiconductor performance and energy efficiency, contributing to more sustainable electronic devices. Through a review of current AI methodologies such as deep learning, support vector machines, and evolutionary computation, the study highlights their potential in optimizing nanomaterial properties and discovering new materials with improved functionalities.
The research methodology combines a systematic review of AI applications in nanomaterial design, with a focus on property prediction and material optimization for semiconductor technologies. It discusses the challenges faced in AI adoption, such as data availability, high computational costs, and algorithm interpretability. Despite these limitations, the paper offers valuable insights and methodologies that can help researchers and engineers in the semiconductor industry apply AI-driven strategies to enhance material discovery, optimize device performance, and reduce energy consumption, ultimately supporting advancements in sustainable electronic technologies.
Authors: Saad, A. M. H.
Journal: International Journal of Nanoelectronics and Materials (IJNeaM)
This research explores the integration of artificial intelligence (AI) in semiconductor nanofabrication, focusing on its potential to enhance design efficiency, process optimization, and overall device performance. By leveraging AI techniques like machine learning and deep learning, the research addresses critical challenges in semiconductor manufacturing, such as miniaturization, complexity, and cost reduction. The study emphasizes AI's role in optimizing processes like lithography, etching, and deposition, as well as predicting material properties and accelerating material discovery. Additionally, AI algorithms can automate defect detection, improving yield and enhancing real-time monitoring to ensure higher manufacturing reliability and efficiency.
The methodology includes a systematic review of AI applications in semiconductor nanofabrication, examining case studies and advancements in process optimization, material discovery, and defect detection. It highlights the challenges of integrating AI into existing workflows, such as data quality issues, the "black box" nature of AI algorithms, and the need for proper integration of AI tools. Despite these limitations, the research demonstrates the transformative potential of AI in overcoming manufacturing hurdles, contributing to the advancement of electronics and energy technologies. This work provides valuable insights for researchers and practitioners in the semiconductor industry, enabling them to implement AI-driven strategies that improve process efficiency, reduce costs, and boost device performance.