Machine Learning for Nano Process Optimization
Manjushree Nayak, Aditya Narayana · 2025
Nano process optimization is crucial for enhancing the performance of nanodevices and materials. This chapter presents a comprehensive review of machine learning (ML) techniques in nanofabrication optimization. It first defines the fundamental problems of nanoscale optimization processes and examines how machine learning can effectively solve these issues. The chapter provides a detailed overview of machine learning techniques used in various aspects of nano process optimization, including design, optimization, and quality control. It also discusses the advantages and limitations of machine learning in this context while addressing future research directions. The ongoing discussion about the potential of machine learning in new nanotechnologies such as nanomedicine and nano-electronics aims to provide researchers and professionals in the nanotechnology industry with a deeper understanding of how machine learning can improve nanotechnology processes to increase efficiency and reliability. Additionally, this chapter aims to increase the efficiency and accuracy of nanofabrication by using machine learning in the optimization process. By combining machine learning algorithms with nanotechnology, it has the potential to revolutionize the design and production of nanoscale materials and devices. The chapter underlines the need for an expert-driven approach and highlights key issues related to trial and error in nano process development. It shows how machine learning models can analyze complex data from nanofabrication processes, revealing subtle patterns and relationships that aid decision-making across products. Furthermore, this chapter explores the potential impact of machine learning-driven process optimization in various industries such as electronics, healthcare, and repair. The integration of machine learning and nanotechnology is expected to lead to advances in information science and engineering. It presents a novel machine learning-based framework for optimizing nanofabrication processes using experimental data and advanced learning methods to achieve process control and optimization. This approach improves process efficiency, throughput, and quality control, ultimately reducing the time and resources required for development and optimization. Through simulations and real-world studies, this chapter demonstrates the role of machine learning in optimizing various nanofabrication processes, providing the potential for rapid and cost-effective production of nanomaterials and nanodevices.