Researchers have developed SABLE, a framework designed to enable large language models (LLMs) to optimize analog circuits within industrial Electronic Design Automation (EDA) flows without compromising sensitive intellectual property. SABLE operates as a closed-loop system, interacting with tools like Cadence Virtuoso, Maestro, and Spectre. It ensures that only scrubbed topology information, performance metrics, and operating summaries are returned to the LLM, maintaining NDA-safe boundaries. The framework was tested on two tasks: a 20 GHz LC-VCO tuning-curve optimization and a two-stage operational amplifier optimization, with several LLM checkpoints successfully completing the tasks within specified iteration limits. AI
IMPACT Enables LLMs to optimize sensitive analog circuits without exposing proprietary design data, potentially accelerating industrial EDA flows.
RANK_REASON The item is a research paper detailing a new framework for LLM-driven circuit optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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