<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ouchen, Rabia</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Djeffal, Faycal</style></author><author><style face="normal" font="default" size="100%">Ferhati, Hichem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning-Guided Design of 10&amp;thinsp;nm Junctionless Gate-All-Around Metal Oxide Semiconductor Field Effect Transistors for Nanoscaled Digital Circuits</style></title><secondary-title><style face="normal" font="default" size="100%">Physica Status Solidi (A) Applications and Materials Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1002/pssa.202400670</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	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–voltage (&lt;i&gt;I&lt;/i&gt;–&lt;i&gt;V&lt;/i&gt;) 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&amp;nbsp;&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;ON&lt;/sub&gt;/&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;OFF&lt;/sub&gt;&amp;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&amp;nbsp;&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;ON&lt;/sub&gt;/&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;OFF&lt;/sub&gt;&amp;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&amp;nbsp;&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;ON&lt;/sub&gt;/&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;OFF&lt;/sub&gt;&amp;nbsp;current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for&amp;nbsp;&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;ON&lt;/sub&gt;/&lt;i&gt;I&lt;/i&gt;&lt;sub&gt;OFF&lt;/sub&gt;&amp;nbsp;and 92.6% for SS, showcasing the model's robustness in these classification tasks.
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