[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127142-en":3,"doc-seo-127142-105":30,"detail-sidebar-cat-0-en-105":91},{"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},127142,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Carbon Nanotube Field Effect Transistor Model Development by Machine Learning","Explores machine learning approaches for developing compact and generative models of carbon nanotube field effect transistors (CNTFETs). Addresses fabrication and performance variability driven by the one-dimensional structure and the need to efficiently navigate large experimental and simulation data sets. Proposes neural network predictors for symmetric devices, including encoding for processing information. Builds a compact model for non-aligned CNT networks using simulation-based inference to extract key parameters, and develops generative models to propose device performance targets and associated processing parameters.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nCarbon Nanotube Field Effect Transistor Model Development by Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/24f095kv](https://escholarship.org/uc/item/24f095kv)  \nAuthor  \nTAN, SHULIN  \nPublication Date  \n2025  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nCarbon Nanotube Field Effect Transistor Model Development by Machine Learning  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Materials Science and Engineering  \nby  \nShulin Tan  \n© Copyright by Shulin Tan  \n2025  \nABSTRACT OF THE DISSERTATION  \nCarbon Nanotube Field Effect Transistor Model Development by Machine Learning  \nby  \nShulin Tan  \nDoctor of Philosophy in Materials Science and Engineering  \nUniversity of California, Los Angeles, 2025  \nProfessor Dwight Christopher Streit, Chair  \nCarbon Nanotube has long been seen as a promising candidate for high-performance electronic material, yet its unique 1D structure leads to challenges in device fabrication. Many processing approaches have been proposed to produce better performing CNTFETsand this explosion of data needs an efficient way to explore. In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance.  \nIn the beginning, I built up a neural network model for CNTFETs. I begin my work with simple cases where only certain continuous parameters like gate length are considered and developed a data cleaning method. It was shown that neural networks can work as a model for CNTFETs and reasonably perform as a device predictor for symmetric field effect transistors. I’ve also developed a neural network model that can incorporate processing information using encoding technique. The model can predict the performance of CNTFETs with various choices of processing methods and material combinations.  \nAt the same time, I explored the conduction properties of non-aligned CNT networks. I built up a compact model for CNTFETs built on non-aligned CNT networks and used simulation-based inference to extract key parameters to fit the model to the experimentally observed data since extraction is impossible through traditional methods. The model with extracted parameters can fit well with the observed data. We show that simulation-based inference can be a powerful tool for building models in cases where a distribution, rather than a certain value, will be the result.  \nIn the last step, I developed a generative model to generate device performance with target current performance. I first built a model to generate three key parameters and built the research on a compact model. The results show that this model can successfully generate multiple solutions that meet the goal. I’ve further developed a generative model that can generate device processing information at the same time. Though further improvement will be needed, some of the targets are met.  \nI hope my work can show the ability of machine learning to solve some of the material science problems. Neural network can be a good function approximator for experimental  \nobservations, though it doesn’t provide understanding of the phenomenon. If probing of mechanism will be needed, simulation-based inference can be a good way to test humancreated models and automatically generate parameters that humans can compare with experimental observations later. This is especially useful when the experiment input or result is a random variable described through the probability mass function or the probability density function. Generative models might be a way for experimental ","cbCaikJynOTuiopP","https://ap.wps.com/l/cbCaikJynOTuiopP","pdf",5764654,1,174,"English","en",105,"# Introduction and Goals\n## Introduction\n## Challenges and Opportunities\n## Thesis structure\n# Background\n## Structures and basic properties of Carbon Nanotubes\n## Characterization of CNTs\n## Carbon Nanotube Field effect transistors (CNTFETs)\n# Introduction to Machine Learning\n## Introduction\n## Introduction of Machine learning\n## Introduction of probabilities\n## Special machine learning techniques used in this thesis\n# Neural Network–based model for CNTFETs\n## Introduction\n## Structure of CNTFETs\n## Neural network with experimental CNTFET data\n## Neural Network model incorporating processing methods","[{\"question\":\"Why is modeling CNTFET performance challenging for device fabrication?\",\"answer\":\"CNTFETs are difficult to optimize because carbon nanotubes have a unique one-dimensional structure, which leads to challenges in fabrication and results in complex performance outcomes.\"},{\"question\":\"What role does neural network modeling play in the thesis?\",\"answer\":\"A neural network model is built to predict CNTFET performance using device parameters, and it can be extended to incorporate processing information through encoding so it can predict performance across different processing choices and material combinations.\"},{\"question\":\"How is simulation-based inference used to build models for non-aligned CNT networks?\",\"answer\":\"A compact CNTFET model is constructed for non-aligned CNT networks, and simulation-based inference extracts key parameters by fitting to experimentally observed data, especially where traditional parameter extraction is not feasible.\"}]","Carbon Nanotube Field Effect Transistor Model Development by Machine Learning | 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is modeling CNTFET performance challenging for device fabrication?","Question",{"text":75,"@type":76},"CNTFETs are difficult to optimize because carbon nanotubes have a unique one-dimensional structure, which leads to challenges in fabrication and results in complex performance outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does neural network modeling play in the thesis?",{"text":80,"@type":76},"A neural network model is built to predict CNTFET performance using device parameters, and it can be extended to incorporate processing information through encoding so it can predict performance across different processing choices and material combinations.",{"name":82,"@type":73,"acceptedAnswer":83},"How is simulation-based inference used to build models for non-aligned CNT networks?",{"text":84,"@type":76},"A compact CNTFET model is constructed for non-aligned CNT networks, and simulation-based inference extracts key parameters by fitting to experimentally observed 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