[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118222-en":3,"doc-seo-118222-105":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},118222,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Modular Machine Learning Based Circuit Design - Improving Scalability of Machine-Learning Based Circuit Design","Traditional circuit design optimizes a pre-selected circuit topology through time-consuming parameter sweeps to meet design criteria. This thesis evaluates a new approach that uses machine learning models to predict the transfer function of a given circuit structure, combined with a genetic algorithm to generate circuits for desired scattering parameters. A key scalability limitation of prior ML models is that they generate only same-size circuits as the training data, motivating larger modular designs from smaller 9×9 examples.","Modular Machine Learning Based Circuit Design  \nImproving scalability of machine-learning based circuit design Master’s thesis in Systems, control and mechatronics, MSc  \nJacob Ekarna, Erik Lind  \nDEPARTMENT OF MICROTECHNOLOGY AND NANOSCIENCE  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2024  \nModular Machine Learning Based Circuit Design  \nImproving scalability of machine-learning based circuit design  \nJacob Ekarna, Erik Lind  \nDepartment of Microtechnology and Nanoscience  \nMC2  \nChalmers accompanied with Ericsson Research Chalmers University of Technology Gothenburg, Sweden 2024  \nModular Machine Learning Based Circuit Design  \nTransforming traditional circuit design: Improving scalability of machine-learning based circuit design  \nJACOB EKARNA ERIK LIND  \n© JACOB EKARNA, ERIK LIND, 2024 .  \nSupervisor: Dr. Martin Sjödin, Ericsson Research  \nExaminer: Prof. Christian Fager, MC2, Chalmers  \nMaster’s Thesis 2024  \nDepartment of Microtechnology and Nanoscience MC2  \nChalmers accompanied with Ericsson Research Chalmers University of Technology  \nSE-412 96 Gothenburg Telephone +46 31 772 1000  \nCover: A modular circuit created by combining four circuit modules in a 2x2 configuration.  \nTypeset in LATEX  \nPrinted by Chalmers Reproservice Gothenburg, Sweden 2024  \nAbstract  \nIn traditional circuit design a pre-selected circuit topology is optimized through time consuming parameter sweeps to satisfy a design criteria. A newly introduced design concept instead utilizes machine learning models to predict the transfer function of a given circuit structure, together with a genetic algorithm to generate a circuit based on wanted scattering parameters.  \nThe concept of machine learning in circuit design, however, has its drawbacks. One notable drawback is that the circuits generated from the model are all of the same size the model was trained on, leading to scalability issues. To overcome this problem this thesis evaluated whether or not it is possible to use a machine learning model trained on a dataset of smaller 9 × 9 circuits to create a larger modular circuit, consisting of four modules. The generated modular circuits were assessed by comparing the predicted scattering parameters from the optimization to the pre-selected target parameters. Additionally, simulations were performed on the generated circuits and the results were compared with the predicted parameters. The thesis also investigated if the implementation of a via fence could help isolate the modules from eachother to reduce electromagnetic interference and improve performance. The differences in time efficiency between the two cases were also compared.  \nThe results show that the modular concept works to a high degree. Based on simulation results, the root mean square error for the scattering parameters for the non-via fence model was 0 .05934 and for the via fence model it was 0 .04677. Adding a via fence improves the model predictions slightly and further improves the simulated circuits significantly. The results for the circuit designs with a via fence, over 100 generated circuits designs, were 13 % more accurate than the circuit designs without a via fence. However, this came at the cost of increased simulation time, as circuits using a via fence took a considerably longer time to simulate.  \nKeywords: Circuit design, Modular, Scattering parameters, Machine Learning, Genetic Algorithm.  \nAcknowledgements  \nWe would like to gratefully acknowledge the support that we have received from our supervisor at Ericsson, Martin Sjödin. Your assistance, encouragement and constant availability during this thesis have been extraordinary, more than what we could have dreamed of. Furthermore we want to express our gratitude to additional colleagues at Ericsson who gave us a warm welcome to their department and aided us with sharp insight into softwares and ideas for our thesis. We would also like to acknowledge ","cbCaiap623Wg5s96","https://ap.wps.com/l/cbCaiap623Wg5s96","pdf",5097424,1,71,"English","en",105,"# Abstract\n## Background and approach\n## Scalability challenge and evaluation scope\n## Via fence impact and performance trade-offs\n## Results summary\n# Keywords\n# Acknowledgements\n# List of Acronyms","[{\"question\":\"How does the thesis approach circuit design differently from traditional methods?\",\"answer\":\"Instead of optimizing a fixed topology using parameter sweeps, it predicts circuit transfer behavior with machine learning and uses a genetic algorithm to generate circuits targeting desired scattering parameters.\"},{\"question\":\"What scalability problem is addressed in the thesis?\",\"answer\":\"The thesis addresses that ML-generated circuits were previously limited to the same size as the training dataset. It tests generating a larger modular circuit composed of four modules using a model trained on smaller 9×9 circuits.\"},{\"question\":\"What is the effect of using a via fence in the modular circuits?\",\"answer\":\"A via fence slightly improves machine learning prediction accuracy and significantly improves simulation results, yielding more accurate designs but requiring longer simulation time.\"}]","Modular Machine Learning Based Circuit Design - Improving Scalability of Machine-Learning Based Circuit Design | PDF",1785682378,179,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"modular-machine-learning-based-circuit-design-improving-scalability-of-machine-learning-based-circuit-design","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/modular-machine-learning-based-circuit-design-improving-scalability-of-machine-learning-based-circuit-design/118222/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis approach circuit design differently from traditional methods?","Question",{"text":75,"@type":76},"Instead of optimizing a fixed topology using parameter sweeps, it predicts circuit transfer behavior with machine learning and uses a genetic algorithm to generate circuits targeting desired scattering parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What scalability problem is addressed in the thesis?",{"text":80,"@type":76},"The thesis addresses that ML-generated circuits were previously limited to the same size as the training dataset. It tests generating a larger modular circuit composed of four modules using a model trained on smaller 9×9 circuits.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the effect of using a via fence in the modular circuits?",{"text":84,"@type":76},"A via fence slightly improves machine learning prediction accuracy and significantly improves simulation results, yielding more accurate designs but requiring longer simulation time.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]