[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124865-en":3,"doc-seo-124865-105":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},124865,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Assisted Design of mmWave Wireless Transceiver Circuits - Master of Science Thesis","Machine learning assisted design is applied to mmWave wireless transceiver circuits through a structured workflow spanning transmitter, receiver, and end-to-end circuit design. The thesis reviews supervised learning and reinforcement learning approaches, then details a 28 GHz transmitter and a 28 GHz receiver architecture with performance specification analyses. Data collection and dataset creation enable model evaluation for selecting ML models and parameters. Ocean scripts implementations support analysis and system-level performance verification, concluding with future directions for automated analog integrated circuit design.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nMachine Learning Assisted Design of mmWave Wireless Transceiver Circuits  \nPermalink  \n[https://escholarship.org/uc/item/6zs6k5xz](https://escholarship.org/uc/item/6zs6k5xz)  \nAuthor  \nZhao, Xuzhe  \nPublication Date  \n2024  \nPeer 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 Wireless Transceiver Circuits  \nTHESIS  \nsubmitted in partial satisfaction of the requirements  \nfor the degree of  \nMASTER OF SCIENCE  \nin Electrical and Computer Engineering  \nby  \nXuzhe Zhao  \nThesis Committee:  \nAssistant Professor Hamidreza Aghasi, Chair Assistant Professor Rahim Esfandyar-Pour Assistant Professor Yanning Shen  \n© 2024 Xuzhe Zhao  \nDEDICATION  \nTo my parents and my friends, for their unwavering support and encouragement.  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES v  \nLIST OF TABLES vi  \nACKNOWLEDGMENTS vii  \nABSTRACT OF THE THESIS viii  \n1 Introduction 1  \n1.1 Background .................................... 1  \n1.2 Motivation ..................................... 3  \n1.3 Organization of This Thesis ........................... 4  \n2 Literature Review 5  \n2.1 Supervised Learning ............................... 8  \n2.2 Reinforcement Learning ............................. 9  \n2.3 Combination of NN and Optimization Algorithms ............... 11  \n3 A 28-GHz Transmitter Design 13  \n3.1 Transmitter Architecture ............................. 14  \n3.2 Performance Specifications Analysis ....................... 15  \n3.2.1 Individual Performance .......................... 15  \n3.2.2 System Performance ........................... 18  \n3.3 Ocean Scripts Implementation .......................... 18  \n4 A 28-GHz Receiver Design 21  \n4.1 Receiver Architecture ............................... 22  \n4.2 Performance Specifications Analysis ....................... 24  \n4.2.1 Individual Performance .......................... 24  \n4.2.2 System Performance ........................... 27  \n4.3 Ocean Scripts Implementation .......................... 28  \n5 Machine Learning Based Transceiver Circuits Design 31  \n5.1 Problem Statement ................................ 32  \n5.2 Data Collection .................................. 32  \n5.3 Model Evaluation ................................. 33  \n6 Conclusion and Future Works 36  \n6.1 Conclusion ..................................... 36  \n6.2 Future Works ................................... 37  \nBibliography 38  \nLIST OF FIGURES  \nPage  \n2.1 Classification for prior automating techniques for circuit synthesis....... 6  \n2.2 Machine learning techniques for analog circuit design reviewed in this thesis. 7  \n2.3 General learning loop for RL [46] ......................... 9  \n2.4 Design flow for RL used in analog circuit design [29] .............. 10  \n2.5 An example of leveraging the knowledge from (a) simpler circuits to (b) more complex one [51] .................................. 12  \n3.1 A 28GHz direct conversion transmitter architecture involving VCO and PA. 14  \n3.2 LC cross-coupled VCO schematic......................... 15  \n3.3 VCO implementation with buffer using NCSU 45nm PDK in Cadence Virtuoso 15  \n3.4 Two-stage differential cascode PA schematic................... 16  \n3.5 PA implementation using NCSU 45nm PDK in Cadence Virtuoso ...... 16  \n3.6 Schematic of target system on transmitter side in Cadence Virtuoso...... 19  \n4.1 A 28GHz heterodyne architecture comprising LNA, mixer and cascode amplifier. 23  \n4.2 Cascode LNA schematic with an inductive degeneration............ 23  \n4.3 LNA implementation with differential configuration using NCSU 45nm PDKin Cadence Virtuoso ............................... 23  \n4.4 Double-balanced Gilbert cell mixer schematic.................. 24  \n4.5 Mixer implementation using NCSU 45nm PDK in Cadence Virtuoso .... 24  \n4.6 Differential cascod","cbCaimUFD8BBgY0D","https://ap.wps.com/l/cbCaimUFD8BBgY0D","pdf",1378040,1,53,"English","en",105,"# Introduction\n## Background\n## Motivation\n## Organization of This Thesis\n# Literature Review\n## Supervised Learning\n## Reinforcement Learning\n## Combination of NN and Optimization Algorithms\n# A 28-GHz Transmitter Design\n## Transmitter Architecture\n## Performance Specifications Analysis\n## Ocean Scripts Implementation\n# A 28-GHz Receiver Design\n## Receiver Architecture\n## Performance Specifications Analysis\n## Ocean Scripts Implementation\n# Machine Learning Based Transceiver Circuits Design\n## Problem Statement\n## Data Collection\n## Model Evaluation\n# Conclusion and Future Works\n## Conclusion\n## Future Works","[{\"question\":\"What circuit types and frequency range does the thesis focus on?\",\"answer\":\"The thesis targets mmWave wireless transceiver circuits, specifically designs for a 28 GHz transmitter and a 28 GHz receiver.\"},{\"question\":\"Which machine learning paradigms are discussed for circuit design?\",\"answer\":\"It covers supervised learning, reinforcement learning, and approaches combining neural networks with optimization algorithms.\"},{\"question\":\"How are simulations and performance analyses carried out in the transmitter and receiver designs?\",\"answer\":\"The work uses performance specification analyses and Ocean scripts implementations to enable system-level analysis and verification for both transmitter and receiver.\"}]","Machine Learning Assisted Design of mmWave Wireless Transceiver Circuits - Master of Science Thesis | 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