A Transfer Learning-Enabled ANN Approach for Cross-Node MOSFET Modeling in Modern Semiconductor Design

A Transfer Learning-Enabled ANN Approach for Cross-Node MOSFET Modeling in Modern Semiconductor Design | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 August 2025 V1 Latest version Share on A Transfer Learning-Enabled ANN Approach for Cross-Node MOSFET Modeling in Modern Semiconductor Design Author : Abinaya S M [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175455558.81399685/v1 225 views 105 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract \received DD MMMM YYYY \acceptedDD MMMM YYYY Accurate extraction of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) parameters is critical for reliable device modeling, advanced circuit simulation, and next-generation semiconductor design. Traditional analytical and optimization-based extraction techniques often involve trade-offs between accuracy, computational efficiency, and robustness under process variations. This research proposes a novel Artificial Neural Network (ANN)-based framework to achieve high-precision parameter extraction for MOSFET devices across a wide range of operating conditions. The framework employs a supervised learning model trained on extensive Technology Computer-Aided Design (TCAD)-generated and experimentally validated datasets, capturing both linear and nonlinear dependencies inherent in transistor behavior. Key MOSFET parameters—such as threshold voltage V TH, mobility degradation factors, subthreshold slope (SS), and short-channel effects (SCE)—are inferred directly from current–voltage (I–V) characteristics using a deep feedforward ANN architecture optimized via regularization and hyperparameter tuning techniques. Unlike conventional regression-based models, the proposed ANN framework generalizes across technology nodes and adapts to process-induced variability, achieving sub-percent deviation from ground truth values. Moreover, the integration of transfer learning enables rapid adaptation to emerging device architectures such as Fin Field-Effect Transistors (FinFETs) and Gate-All-Around Field-Effect Transistors (GAAFETs) without requiring complete retraining. Extensive validation on 65 nm and 28 nm Complementary Metal-Oxide-Semiconductor (CMOS) technologies demonstrates superior performance over conventional SPICE-based extraction and statistical fitting methods in terms of accuracy, scalability, and computational efficiency. This work highlights the potential of AI-driven modeling frameworks to automate design processes, shorten development cycles, and enhance predictive capabilities in advanced semiconductor design environments. Future work will explore hybrid neural-symbolic models that combine data-driven learning with physics-based reasoning to improve interpretability and rule compliance. Integration with process-aware compact models is also planned to enhance EDA tool compatibility and support advanced node optimization. Supplementary Material File (manuscript.docx) Download 1.80 MB Information & Authors Information Version history V1 Version 1 07 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ann deep learning mosfet parameter extraction transfer learning Authors Affiliations Abinaya S M [email protected] SIMATS Deemed University View all articles by this author Metrics & Citations Metrics Article Usage 225 views 105 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Abinaya S M. A Transfer Learning-Enabled ANN Approach for Cross-Node MOSFET Modeling in Modern Semiconductor Design. Authorea . 07 August 2025. DOI: https://doi.org/10.22541/au.175455558.81399685/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175455558.81399685/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffc61cdbe2352ad',t:'MTc3OTQ1ODc5Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();