[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118489-en":3,"doc-seo-118489-105":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},118489,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting MOSFET Aging with Machine Learning - Master of Science Thesis","This thesis presents a machine learning model designed to predict the age of a given MOSFET device. The work documents the full design workflow, including data collection and setup, model design and parameter selection, and iterative refinement across multiple test runs. Detailed discussions cover data formatting, engineered values, hyperparameters, and observed outcomes from testing, with emphasis on practical steps and results. Basic knowledge of Virtuoso and Python is assumed.","Predicting MOSFET Aging with Machine  \nLearning  \nThomas Youhn  \nA creative component submitted to the graduate faculty  \nIn partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Electrical Engineering  \nProgram of Study Committee:  \nDegang Chen, Major Professor  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this creative component. The Graduate College will ensure this creative component is globally accessible and will not permit alterations  \nafter a degree is conferred.  \nIowa State University  \nAmes, Iowa  \n2025  \nCopyright © Thomas Youhn, 2025. All rights reserved.  \nContents  \nAbstract ........................................................................................................................................... 3  \nIntroduction ..................................................................................................................................... 4  \nStatement of Purpose .................................................................................................................. 5  \nVirtuoso Data Collection................................................................................................................. 6  \nTesting Circuit............................................................................................................................. 6  \nMaestro Setup ............................................................................................................................. 8  \nResults ....................................................................................................................................... 10  \nMachine Learning Model Design ................................................................................................. 13  \nData Formatting ........................................................................................................................ 13  \nModel Parameters ..................................................................................................................... 15  \nHyperparameters ................................................................................................................... 16  \nEngineered Values ................................................................................................................. 18  \nFirst Test Results ....................................................................................................................... 18  \nModel Refinement ........................................................................................................................ 22  \nSecond Run Changes ................................................................................................................ 22  \nSecond Run Results .................................................................................................................. 24  \nFinal Run................................................................................................................................... 29  \nFinal Run Results ...................................................................................................................... 29  \nConclusion .................................................................................................................................... 32  \nReferences ..................................................................................................................................... 33  \nAbstract  \nThis paper reviews my work on my Master’s Program research project, which involved creating a machine learning model that could predict the age ofa given MOSFET device. My work on this project contributed to the creative component of my program of study. The report goes over my design process for setting up and collecting the data, designing the machine learning model, and refining the model","cbCaitUeoz9zD9JL","https://ap.wps.com/l/cbCaitUeoz9zD9JL","pdf",998647,1,33,"English","en",105,"# Contents\n## Abstract\n## Introduction\n## Statement of Purpose\n## Virtuoso Data Collection\n## Testing Circuit\n## Maestro Setup\n## Results\n## Machine Learning Model Design\n## Data Formatting\n## Model Parameters\n## Hyperparameters\n## Engineered Values\n## First Test Results\n## Model Refinement\n## Second Run Changes\n## Second Run Results\n## Final Run\n## Final Run Results\n## Conclusion\n## References","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To create a machine learning model that can predict the age of a specific MOSFET device based on collected and processed test data.\"},{\"question\":\"Which aging mechanisms are emphasized in the study?\",\"answer\":\"Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI) are discussed as common processes that degrade MOSFET characteristics over time.\"},{\"question\":\"What parts of the workflow are covered beyond model building?\",\"answer\":\"The thesis covers the end-to-end process: data collection and setup, testing circuit configuration, data formatting and engineered values, and iterative model refinement with results from multiple runs.\"}]","Predicting MOSFET Aging with Machine Learning - 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