[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118466-en":3,"doc-seo-118466-105":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},118466,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Assisted Design of mmWave Radio Frequency Circuits - Thesis","This thesis develops a machine-learning assisted workflow for designing mmWave radio frequency circuits, targeting key building blocks including multistage differential amplifiers, VCOs, and mixers. It reviews supervised and reinforcement learning approaches and ML model families used for circuit parameter prediction. The work constructs datasets for homogeneous and heterogeneous circuits, trains multiple models (MLP, Transformer, and classical regressors such as RF, kNN, and SVR), and evaluates performance using simulation-based metrics. Results provide insights into accuracy, model selection with circuit complexity, and scalability across circuit types.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nMachine Learning Assisted Design of mmWave Radio Frequency Circuits  \nPermalink  \n[https://escholarship.org/uc/item/5fg6r9pf](https://escholarship.org/uc/item/5fg6r9pf)  \nAuthor  \nVillasenor, Andrea  \nPublication Date  \n2025  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nMachine Learning Assisted Design of mmWave Radio Frequency Circuits  \nTHESIS  \nsubmitted in partial satisfaction of the requirements  \nfor the degree of  \nMASTER OF SCIENCE  \nin Electrical and Computer Engineering  \nby  \nAndrea Villasenor  \nThesis Committee:  \nAssistant Professor Hamidreza Aghasi, Chair Assistant Professor Yanning Shen Assistant Professor Mohsen Imani  \n© 2025 Andrea Villasenor  \nDEDICATION  \nTo my parents, siblings, and friends for their unconditional support and encouragement.  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES v  \nLIST OF TABLES vii  \nLIST OF ALGORITHMS viii  \nACKNOWLEDGMENTS ix  \nABSTRACT OF THE THESIS x  \n1 Introduction 1  \n1.1 Background .................................... 1  \n1.2 Motivation ..................................... 3  \n1.3 Organization of This Thesis ........................... 5  \n2 Literature Review 6  \n2.1 Supervised Learning ............................... 6  \n2.2 Reinforcement Learning ............................. 8  \n2.3 ML Models .................................... 9  \n3 Multistage Differential Amplifier Circuit Design 10  \n3.1 Multistage Differential Amplifier Circuit Architecture ............. 10  \n3.2 Multistage Differential Amplifier Simulation .................. 13  \n4 VCO Circuit Design 17  \n4.1 VCO Literature Review ............................. 17  \n4.2 Ring Oscillator Circuit Architecture ....................... 19  \n4.3 Ring Oscillator Performance Analysis ...................... 20  \n5 Mixer Circuit Design 24  \n5.1 Mixer Circuit Literature ............................. 24  \n5.2 Mixer Circuit Architecture ............................ 25  \n5.3 Mixer Circuit Performance Analysis ....................... 26  \n6 ML-Assisted Design Pipeline 32  \n6.1 Problem Statement ................................ 32  \n6.1.1 Analytical ML Perspective ........................ 34  \n6.1.2 Application to Circuit Schematic Design ................ 36  \n6.2 Dataset ...................................... 37  \n6.2.1 Datasets for Homogeneous Circuits ................... 37  \n6.2.2 Dataset Collection Procedure ...................... 38  \n6.3 Methodology ................................... 40  \n6.3.1 ML Models for Circuit Parameter Prediction .............. 41  \n6.3.2 MLP .................................... 41  \n6.3.3 Transformer Model ............................ 42  \n6.3.4 RF ..................................... 44  \n6.3.5 kNN .................................... 44  \n6.3.6 SVR .................................... 45  \n6.3.7 Metrics .................................. 46  \n6.3.8 Model Evaluation Metrics ........................ 46  \n6.3.9 Model Training and Simulation-Based Evaluation ........... 48  \n7 Conclusion and Future Works 50  \n7.1 Preliminary Results and Discussion ....................... 50  \n7.1.1 Mixer Evaluation and Model Performance Analysis .......... 50  \n7.1.2 VCO Evaluation and Model Performance Analysis ........... 52  \n7.2 Machine Learning in Circuit Optimization: Insights and Performance Analysis 54  \n7.2.1 Analytical Perspective on ML Model Performance ........... 55  \n7.2.2 Model Selection Based on Circuit Complexity ............. 55  \n7.2.3 Scalability Across Homogeneous and Heterogeneous Circuits ..... 56  \n7.3","cbCaikR4JJyF82Ql","https://ap.wps.com/l/cbCaikR4JJyF82Ql","pdf",3214694,1,78,"English","en",105,"# Introduction\n## Background\n## Motivation\n## Organization of This Thesis\n# Literature Review\n## Supervised Learning\n## Reinforcement Learning\n## ML Models\n# Multistage Differential Amplifier Circuit Design\n## Multistage Differential Amplifier Circuit Architecture\n## Multistage Differential Amplifier Simulation\n# VCO Circuit Design\n## VCO Literature Review\n## Ring Oscillator Circuit Architecture\n## Ring Oscillator Performance Analysis\n# Mixer Circuit Design\n## Mixer Circuit Literature\n## Mixer Circuit Architecture\n## Mixer Circuit Performance Analysis\n# ML-Assisted Design Pipeline\n## Problem Statement\n## Dataset\n## Methodology\n# Conclusion and Future Works\n## Preliminary Results and Discussion\n## Machine Learning in Circuit Optimization: Insights and Performance Analysis\n## Conclusion and Future Directions","[{\"question\":\"What circuit types does the thesis focus on designing with machine learning assistance?\",\"answer\":\"The thesis focuses on multistage differential amplifier circuits, VCO circuits (ring oscillators), and mixer circuits, analyzing their architecture and performance.\"},{\"question\":\"How is the dataset for ML training constructed in the proposed pipeline?\",\"answer\":\"The pipeline includes dataset generation for homogeneous and heterogeneous circuits, with a defined dataset collection procedure to support circuit parameter learning.\"},{\"question\":\"Which machine learning models are used for predicting circuit parameters and how are they evaluated?\",\"answer\":\"The thesis uses models including MLP and Transformer, as well as RF, kNN, and SVR, and evaluates them using simulation-based training and model evaluation metrics.\"}]","Machine Learning Assisted Design of mmWave Radio Frequency Circuits - Thesis | 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