Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning

Adopting machine learning (ML)–based techniques in hybrid nanodevice fabrication represents a paradigm shift for precision control that provides a route toward defect minimization. This chapter summarizes the ML automation in nanosensors fabrication, specifically focusing on recent advances in biomedical nanosensors where ML-related optimization of nanosensors fabrication yielded improved detection sensitivity and selectivity. In this chapter, we will discuss the application of advanced ML-based models, including deep neural networks, convolutional neural networks, and reinforcement learning algorithms in improving the performance of fabrication (deposition, etching, and lithography) steps. It also illustrates the game-changing impact of ML in facilitating accurate control and minimizing defects, which lays the foundation for upcoming advances in nanotechnology. This chapter highlights these real-world applications of ML and presents a detailed framework for implementing such techniques within the manufacturing process of hybrid nanodevices.