[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120771-en":3,"doc-seo-120771-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120771,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Green’s Function Simulations","A novel simulation workflow combines convolutional generative machine learning with quantum-mechanical non-equilibrium Green’s function (NEGF) device modeling. The method, ML-NEGF, is implemented in the NESS (nano-electronics simulations software) in-house simulator and targets faster convergence for coupled Poisson–NEGF computations. Training enables the model to learn the underlying physics of nano-sheet transistor behavior while maintaining accuracy. Results show an average convergence acceleration of 60%, substantially reducing computational time compared with standard NEGF.","Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Green’s  \nFunction Simulations  \nPreslav Aleksandrov Device Modelling Group James Watt School of Engineering University of Glasgow, UK [preslav.aleksandrov@glasgow.ac.uk](preslav.aleksandrov@glasgow.ac.uk)  \nTapas Dutta Device Modelling Group James Watt School of Engineering University of Glasgow, UK [tapas.dutta@glasgow.ac.uk](tapas.dutta@glasgow.ac.uk)  \nAli Rezaei Device Modelling Group James Watt School of Engineering University of Glasgow, UK [ali.reazaei@glasgow.ac.uk](ali.reazaei@glasgow.ac.uk)  \nAsen Asenov Device Modelling Group James Watt School of Engineering University of Glasgow, UK[asen.asenov@glasgow.ac.uk](asen.asenov@glasgow.ac.uk)  \nNikolas Xeni Device Modelling Group James Watt School of Engineering University of Glasgow, UK [nikolas.xeni@glasgow.ac.uk](nikolas.xeni@glasgow.ac.uk)  \nVihar Georgiev Device Modelling Group James Watt School of Engineering University of Glasgow, UK [vihar.georgiev@glasgow.ac.uk](vihar.georgiev@glasgow.ac.uk)  \nAbstract—This work describes a novel simulation approach that combines machine learning and device modeling simulations. The device simulations are based on the quantum mechanical non-equilibrium Green’s function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the ‘standard’ NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our MLNEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.  \nKeywords— machine learning, neural network, autoencoder, device simulations and modeling, non-equilibrium Green’s function (NEGF), TCAD device modeling, nanowires.  \nI. INTRODUCTION  \nThe silicon nanowire and nanosheet transistors have a wide spectrum of promising applications [1], such as current field-effect transistors [2] and photovoltaics [3] . Moreover, the state-of-the-art CMOS technologies are based on single or stacked configurations of nanosheet or nanowire architectures [4] . However, despite the recent advances in technology, there is still room to improve the fabrication process and to optimise device performance, for example, by reducing power consumption and reducing device-to-device variability during the fabrication process.  \nFrom a practical point of view, simulations and modelling transistors are the most time-efficient and cost-effective approach to evaluate the performance and the output characteristics of transistors. The aim is to have a simulation platform that is fast, accurate, and reliable in order to aid the improvement of device design, predict device performance (current-voltage characteristics) and extract important Figures of Merit (FoM) .  \nThe main aim of this work is to investigate the possibility of significantly improving or even replacing numerical  \nFig. 1. Diagram of the n-type silicon (Si) nanosheet transistor with channel cross section of 3 nm x 12 nm (a) and length of full device 22 nm (b). Gate length is 16 nm and the source/drain have length of 3 nm each. The channel doping is 1e16 cm-3 and the contacts (source and drain have) are 1e20 cm-3. The oxide is SiO2 with thickness of 1nm everywhere around the device.  \nTechnology Computer-Aided Design (TCAD) device simulations with a convolutional autoencoder (CAE) [5] [6]  \n[7] . To test our idea, we have developed a new simulation approach based on the combination of TCAD and machine learning methods. The current state of the art of TCAD simulations is based on th","cbCaioFKCIjJJ88u","https://ap.wps.com/l/cbCaioFKCIjJJ88u","pdf",472180,1,4,"English","en",105,"# Introduction\n# Device Structure\n# Simulations Methodology","[{\"question\":\"What problem does ML-NEGF address in NEGF device simulations?\",\"answer\":\"ML-NEGF improves the convergence speed of NEGF-based coupled Poisson–NEGF simulations, reducing overall computational time while keeping accuracy.\"},{\"question\":\"What underlying simulation and learning components does the method combine?\",\"answer\":\"The device simulation relies on the quantum-mechanical non-equilibrium Green’s function (NEGF) formalism, while the learning component extends a convolutional generative network (convolutional autoencoder-based approach).\"},{\"question\":\"How much convergence acceleration is reported compared with standard NEGF?\",\"answer\":\"The ML-NEGF approach achieves an average convergence acceleration of 60% versus the standard NEGF approach.\"}]","Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Green’s Function Simulations | PDF",1785731951,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"fully-convolutional-generative-machine-learning-method-for-accelerating-non-equilibrium-greens-function-simulations","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/fully-convolutional-generative-machine-learning-method-for-accelerating-non-equilibrium-greens-function-simulations/120771/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does ML-NEGF address in NEGF device simulations?","Question",{"text":74,"@type":75},"ML-NEGF improves the convergence speed of NEGF-based coupled Poisson–NEGF simulations, reducing overall computational time while keeping accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What underlying simulation and learning components does the method combine?",{"text":79,"@type":75},"The device simulation relies on the quantum-mechanical non-equilibrium Green’s function (NEGF) formalism, while the learning component extends a convolutional generative network (convolutional autoencoder-based approach).",{"name":81,"@type":72,"acceptedAnswer":82},"How much convergence acceleration is reported compared with standard NEGF?",{"text":83,"@type":75},"The ML-NEGF approach achieves an average convergence acceleration of 60% versus the standard NEGF approach.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]