Reliable and efficient PdM schemes for nanoelectronics

The progress of nanoelectronics has raised a problem of the maintenance of this equipment and minimization of their breakdown. The chapter proposes a framework to improve maintenance performance by using machine-learning-based algorithms in conjunction with big-data-based failure prediction on nanoelectronics technologies. Information (data) from nanoelectronic systems for neural networks using the decision trees for observation of the past and real-time data for fault monitoring. The learned data-driven models allow us to learn system dynamics that we were not able to bring up by previous state-of-the-art methods. Experimental results also showed that the reliability device was enhanced, the operation cost budget was minimized, and equipment downtime was reduced. The results and discussion show the advantages of using artificial intelligence (AI)-based maintenance and optimization systems concerning the improvement of the life performance of nanoelectronic systems. AI and sensor analytics might help pinpoint failures more precisely, transforming it into a tool for reliability engineering. So live applications and improvements in AI models for improved accuracy of forecast and efficiency in the nanoelectronics maintenance systems may be performed in the future.