Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices

This book chapter examines the progress made in optimizing the fabrication of semiconductor nanodevices by using supervised learning models and predictive metrology techniques. The evolution of integrated circuits has led to a remarkable miniaturization of components, greatly improving circuit performance. This trend has been evident since the shift from vacuum tubes to transistors. The study emphasizes the efficacy of machine learning models, capable of minimizing sampling needs to just 30% while ensuring high accuracy in outcomes. Methods such as scatterometry, along with principal component analysis and neural networks, are utilized to forecast critical dimensions across different applications, including mask fabrication. Moreover, the document highlights the significance of predictive metrology in optimizing the lithography process by allowing for the early identification of parameter variations, thereby enabling prompt adjustments prior to electrical testing. The incorporation of these sophisticated methodologies significantly improves the efficiency of semiconductor fabrication and facilitates the continuous advancement of electronic circuits in a society that is becoming more intelligent.