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AI agents generate analog circuit layouts using novel frameworks

Two new research papers explore the use of AI agents for analog circuit layout generation. The first paper, "The Code Behind the Layout," investigates how AI agents can write generator code for frameworks like gLayout, finding that this approach is more efficient and reusable than direct layout generation. The second paper, "OPERA: OPamp Design by an Expert-Taught Reasoning Agent," details an AI agent that designs CMOS opamps from specifications to post-layout sign-off using the gm/ID methodology and the gLayout framework in the Sky130 process. AI

IMPACT These advancements suggest AI agents can significantly streamline and improve the efficiency of analog circuit design and layout processes.

RANK_REASON Two academic papers published on arXiv detailing novel AI agent applications for analog circuit layout generation.

Read on arXiv cs.AI →

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AI agents generate analog circuit layouts using novel frameworks

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Two academic papers published on arXiv detailing novel AI agent applications for analog circuit layout generation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Minxing Chu, Anhang Li, Martins Atilla, Ruichen Qi, Mehdi Saligane ·

    The Code Behind the Layout: AI Agents for Analog Layout Generators

    arXiv:2610.38426v1 Announce Type: cross Abstract: Analog layout remains largely manual and hard to automate. Generator frameworks such as gLayout express layouts as programs, producing layouts from parameterized, design-rule-aware generators; however, writing such generators stil…

  2. arXiv cs.AI TIER_1 English(EN) · Vishal Saxena, Soumyajit Mandal ·

    OPERA: OPamp Design by an Expert-Taught Reasoning Agent, from Specifications to Layout

    arXiv:2610.39395v1 Announce Type: cross Abstract: Analog flows from specifications to a signed-off layout rarely run without manual iteration. Recent large language model (LLM) agents size circuits with optimizer loops over the device dimensions and stop at the schematic. We pres…