A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication

The ability of systems to learn from data and get better without explicit programming is what machine learning (ML) has done to change current technology. Into three categories—supervised, unsupervised, and reinforcement learning—this work provides a thorough overview of important ML methods. It investigates the benefits, drawbacks, and uses of well-known algorithms like decision trees, support vector machines, k-means clustering, and deep learning models such as convolutional neural networks in image recognition, natural language processing, and predictive analytics. These algorithms’ performance is evaluated with an emphasis on real-world problems and practical applications, taking into account variables such as accuracy, computing efficiency, and scalability. This chapter also explores the deployment of ML in the enhancement of nanodevice fabrication, such as enhanced design processes with optimized accuracy and reduced development time. Use of ML algorithms helps to explore difficult-to-navigate design spaces and forecast fabrication outcomes, ensuring that nanostructures fabricated on different occasions are more reliable and are having higher efficiency.