Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication

Unsupervised machine learning (UML) techniques are changing the field of nanodevice manufacture as a step toward challenging design difficulties in addition to improved precision at the nanoscale. In this study, attempts have been made to improve the manufacturing process using UML techniques such as clustering, dimensionality reduction, and anomaly detection for nanophotonic and nanoelectronic devices. The geometries of nanostructures have been optimized for increased optical and electrical performance, while minimizing defects, by using inverse design along with deep neural networks during the manufacturing process. UML can provide a comprehensive structural analysis by combining four-dimensional scanning transmission electron microscopy along with machine learning–based image processing, which gives some very important insights into material behavior and fabrication-induced variations. Reinforcement learning and generative adversarial networks (GANs) have demonstrated their ability to automate nanoscale manufacturing, reduce development cycles, and scale up. These experiments demonstrate considerable yield improvement, efficiency, and mitigation of defects with applications toward quantum devices, optoelectronics, and advanced complementary metal–oxide–semiconductor (CMOS) technologies. This work displays the potential of UML toward changing nanodevice creation for scaled solutions toward future applications of nanotechnology. The present outcome has further avenues that require further research in this context of hybrid AI framework with real-time integration applications within high-precision environments in industries.