[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-56260-en":3,"doc-seo-56260-105":29,"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},56260,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",6,"Technology","Artificial Intelligence Assisted Analog IC Design Bridging Human Intuition and Machine Intelligence","Artificial intelligence-assisted methodologies for analog integrated-circuit (IC) design address the high expertise demand and time-intensive nature of traditional analog flows. The text analyzes why analog design lags behind digital automation, focusing on tightly coupled choices from specification interpretation and topology exploration to device sizing, simulation refinement, and layout verification. It reviews state-of-the-art AI approaches across key stages—especially block-level topology exploration, parameter optimization, and layout—showing how they can improve efficiency and robustness while clarifying practical limitations and where AI falls short.","By Souradip Poddar, Bingyang Liu, Seunggeun Kim, and David Z. Pan  \nArtificial IntelligenceAssisted AnalogIC Design  \nBridging human intuition and machine intelligence  \nA MS circuits qui  \netly underpin  \nmuch of today’s technology, powering everything  \nfrom the Internet of Things and edge  \nDigital Object Identifier 10. 1109/MSSC.2026.3678510 Date of current version: 23 June 2026  \ncomputing to autonomous vehicles, 5G communication, and wireless sensing [1]. Yet behind this ubiquitous presence lies a design process that remains among the most expert-reliant and time-intensive stages of modern semiconductor development. Designers must navigate tightly coupled decisions spanning specification in-  \nterpretation, topology exploration, device sizing, physical layout, and simulation-driven refinement [2], often under aggressive time-to-market pressure and growing system complexity. Unlike digital design, which benefits from decades of mature and standardized automation tools, AMS design has followed a very different  \npath. Much of today’s analog design flow still hinges on manual reasoning and extensive simulation loops, a reliance that makes it vulnerable to slow turnaround, fragile design choices, aggressive overdesign, and a strong dependence on individual designer experience, intuition, and design style, ultimately limiting scalability. Figure 1 illustrates a high-level conceptual view of the major stages in analog IC design.  \nRecent advances in machine learning and large language models (LLMs) have demonstrated immense capabilities in code generation, data analysis, and pattern recognition. More recently, their emerging cognitive reasoning and agentic behaviors [3], [4], [5], [6] suggest the potential to emulate key aspects of human design workflows. As these technologies begin to enter the analog design space with growing promise, a natural question emerges:“ Where does AI [artificial intelligence] truly help, and where does it fall short?” To answer this question meaningfully, it is essential to examine the distinct challenges across different stages of the analog design flow, and to assess where AI can and cannot provide practical value. In the remainder of this article, we review various state-of-the-art AI-driven methodologies across key stages of the design process, particularly block-level topology exploration, circuit parameter optimization, and layout, as summarized in Figure 2, focusing on how these approaches can enhance design efficiency, provide systematic assistance to designers, and enhance the overall robustness of the design flow, while also highlighting their practical limitations in real-world design scenarios.  \nTopology Exploration  \nPerformance-centric topology optimization lies at the heart of successful analog front-end design. At this early stage, designers must identify architectures with the inherent capability to satisfy performance targets, since a topology that is fundamentally misaligned with the design scenario becomes extremely difficult, if not impractical, to recover through  \nWhat makes topology exploration challenging is the tight coupling between architectural choicesand device sizing.  \nsubsequent parameter tuning. When these early choices miss the mark, design effort often shifts toward excessive simulation effort and aggressive overdesign, leading to potential loopbacks and extended development cycles. For this reason, this stage stands as one of the most consequential in the analog front-end design flow.  \nWhat makes topology exploration challenging is the tight coupling between architectural choicesand device sizing. The performance that a circuit can ultimately achieve depends not only on the chosen topology but also on how devices are dimensioned, while the range of viable circuit parameters is itself dictated by the architectural structure. As a result, optimizing topology and circuit parameters in isolation is rarely effective in practice, placing a heavy burden on designer j","cbCaipICREtR9J16","https://ap.wps.com/l/cbCaipICREtR9J16","pdf",1465898,1,11,"English","en",105,"# Topology Exploration\n## Architectural coupling with device sizing\n## AI-assisted approaches for early-stage support","[{\"question\":\"Why is analog IC topology exploration particularly challenging?\",\"answer\":\"Topology exploration is challenging because architectural choices are tightly coupled with device sizing. When early choices miss the mark, subsequent tuning requires excessive simulation and can trigger loopbacks and extended development cycles.\"},{\"question\":\"How can AI assist topology exploration in analog design?\",\"answer\":\"AI can narrow the search space by identifying and ranking candidate architectures likely to meet performance targets. It supports designers by reducing reliance on exhaustive manual exploration and improving systematic guidance during early-stage decisions.\"},{\"question\":\"What are two broad directions for existing AI-assisted topology exploration methods?\",\"answer\":\"Methods fall into topology selection from predefined libraries, which reduces risk using proven architectures, and topology generation, where AI proposes novel circuit structures but may introduce reliability and silicon-behavior predictability challenges.\"}]",1783803969,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"artificial-intelligence-assisted-analog-ic-design-bridging-human-intuition-and-machine-intelligence","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/artificial-intelligence-assisted-analog-ic-design-bridging-human-intuition-and-machine-intelligence/56260/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-11",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is analog IC topology exploration particularly challenging?","Question",{"text":75,"@type":76},"Topology exploration is challenging because architectural choices are tightly coupled with device sizing. When early choices miss the mark, subsequent tuning requires excessive simulation and can trigger loopbacks and extended development cycles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How can AI assist topology exploration in analog design?",{"text":80,"@type":76},"AI can narrow the search space by identifying and ranking candidate architectures likely to meet performance targets. It supports designers by reducing reliance on exhaustive manual exploration and improving systematic guidance during early-stage decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"What are two broad directions for existing AI-assisted topology exploration methods?",{"text":84,"@type":76},"Methods fall into topology selection from predefined libraries, which reduces risk using proven architectures, and topology generation, where AI proposes novel circuit structures but may introduce reliability and silicon-behavior predictability challenges.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]