@article {104938,
	title = {Machine Learning-Guided Design of 10\&thinsp;nm Junctionless Gate-All-Around Metal Oxide Semiconductor Field Effect Transistors for Nanoscaled Digital Circuits},
	journal = {Physica Status Solidi (A) Applications and Materials Science},
	year = {2024},
	abstract = {
	In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current{\textendash}voltage (I{\textendash}V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and\&nbsp;ION/IOFF\&nbsp;ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting\&nbsp;ION/IOFF\&nbsp;current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both\&nbsp;ION/IOFF\&nbsp;current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8\% for\&nbsp;ION/IOFF\&nbsp;and 92.6\% for SS, showcasing the model{\textquoteright}s robustness in these classification tasks.

},
	url = {https://doi.org/10.1002/pssa.202400670},
	author = {Ouchen, Rabia and Berghout, Tarek and Djeffal, Faycal and Ferhati, Hichem}
}
