[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120470-en":3,"doc-seo-120470-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120470,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Compressed SPICE-Compliant IC Models via Machine Learning Kernel Regression","This paper presents a fully behavioral machine learning methodology to generate compact, accurate models of IC buffers. The method uses a vector-valued implementation of kernel Ridge regression, building models from measured transient responses collected during normal device operation. It focuses on an efficient compression strategy that reduces model complexity by lowering the number of regression coefficients. The resulting compact representation can be integrated into SPICE-based solvers to support fast signal-integrity and high-speed channel analysis.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nCompressed SPICE-Compliant IC Models via Machine Learning Kernel Regression  \nOriginal  \nCompressed SPICE-Compliant IC Models via Machine Learning Kernel Regression / Atlante, Marco; Trinchero, Riccardo; Bradde, Tommaso; Manfredi, Paolo; Stievano, Igor S.. -ELETTRONICO. - (2024), pp. 1-3. (Intervento presentato al convegno IEEE Electrical Design of Advanced Packaging and Systems (EDAPS) tenutosi a Bangalore (Ind) nel 17-19 December 2024) [10 . 1109/edaps64431 .2024. 10988464] .  \nAvailability:  \nThis version is available at: 11583/3000088 since: 2025-05-13T07:35:41Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/edaps64431.2024.10988464  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n04 October 2025  \nCompressed SPICE-compliant IC Models via Machine Learning Kernel Regression  \nMarco Atlante 1  \nDept. Electronics and Telecomm. Politecnico di Torino Torino, Italy marco.atlante@polito.it  \nRiccardo Trinchero2  \nDept. Electronics and Telecomm. Politecnico di Torino Torino, Italy riccardo.trinchero@polito.it  \nTommaso Bradde3  \nDept. Electronics and Telecomm. Politecnico di Torino Torino, Italy tommaso.bradde@polito.it  \nPaolo Manfredi4  \nDept. Electronics and Telecomm. Politecnico di Torino Torino, Italy paolo.manfredi@polito.it  \nIgor S. Stievano5 Dept. Electronics and Telecomm. Politecnico di Torino Torino, Italy igor.stievano@polito.it  \nAbstract—This paper introduces a fully behavioral machine learning methodology for generating compact and accurate models of IC buffers. The proposed approach leverages a vectorvalued implementation of the kernel Ridge regression to construct models based on observations of device transient responses recorded during normal operation. A key focus is placed on developing an efficient compression scheme to minimize model complexity (i.e., the number of regression coefficients), resulting in a compact mathematical representation that can be efficiently integrated into any SPICE-based solver.  \nIndex Terms—digital integrated circuits, buffer modeling, signal integrity, high-speed interconnects, kernel regression.  \nI. INTRODUCTION  \nFor decades, the signal and power integrity community has focused on the development of accurate and efficient simulation models of digital IC buffers to be used for the quality and reliability assessment of high-speed digital channels.  \nThis trend has been largely driven by the development of the Input/Output Buffer Information Specification (IBIS), supported by electronic design automation (EDA) tools and silicon vendors [1] . IBIS promotes a modeling approach based on fundamental building blocks representing the functionalities of key elements within IC buffers. Despite the widespread adoption of IBIS, the research community has continued to explore alternative solutions aimed at enhancing accuracy and/or simplifying the model generation process [2]–[4] .  \nWith the rapid advancement of machine learning (ML) techniques, behavioral modeling approaches for IC buffers based on recurrent neural networks (RNNs) have gained significant attention due to their structural flexibility and accuracy [5],[6] . On the other hand, the training of such kind of model can be computationally expensive, since it requires the solution of anon-convex optimization problem and the tuning of several hyperparameters (e.g., number of layer, number of neuron, etc...) . Moreover, due to their i","cbCaivy1UgCFpNN2","https://ap.wps.com/l/cbCaivy1UgCFpNN2","pdf",516216,1,4,"English","en",105,"# Abstract\n# Index Terms\n# I. Introduction\n# II. IC Modeling via Kernel Ridge Regression","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses generating accurate and efficient simulation models of IC buffers that can be used in SPICE-based workflows for signal and power integrity analysis.\"},{\"question\":\"How does the proposed modeling method work?\",\"answer\":\"It builds a behavioral model using vector-valued kernel Ridge regression trained on observations of the IC port transient responses collected during normal operation.\"},{\"question\":\"What is the main contribution regarding model complexity?\",\"answer\":\"It proposes an efficient compression scheme that reduces the number of regression coefficients, producing a compact model representation suitable for integration into SPICE compliant solvers.\"}]","Compressed SPICE-Compliant IC Models via Machine Learning Kernel Regression | PDF",1785730257,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"compressed-spice-compliant-ic-models-via-machine-learning-kernel-regression","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/compressed-spice-compliant-ic-models-via-machine-learning-kernel-regression/120470/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address?","Question",{"text":74,"@type":75},"It addresses generating accurate and efficient simulation models of IC buffers that can be used in SPICE-based workflows for signal and power integrity analysis.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed modeling method work?",{"text":79,"@type":75},"It builds a behavioral model using vector-valued kernel Ridge regression trained on observations of the IC port transient responses collected during normal operation.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the main contribution regarding model complexity?",{"text":83,"@type":75},"It proposes an efficient compression scheme that reduces the number of regression coefficients, producing a compact model representation suitable for integration into SPICE compliant solvers.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]