arXiv AI By Vishal Saxena, Soumyajit Mandal

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

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OPERA is an expert‑taught reasoning agent that uses a large language model to design CMOS opamps from specifications to post‑layout sign‑off with open‑source tools. The agent first learns the gm/ID sizing method, then the gLayout framework, and writes a reusable layout generator that closes the layout‑extraction loop. In the SKY130 process, OPERA designed a shared constant‑gm bias and six different opamps that meet all target specifications across 15 PVT corners, with DRC‑clean and LVS‑matched layouts.

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arXiv AI
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Analog-DB: An Agent-First Analog Integrated Circuit Database, From Blocks to Systems

Analog-DB is an open‑source, versioned database that stores analog integrated circuit designs in a process‑neutral topology, reusable testbenches, and machine‑readable datasheets using a domain‑specific language. The database’s parameterization scheme and schema‑governed contracts enable AI design agents to discover, compose, and retarget circuits across multiple process kits. In a case study, a coding agent used the released artifacts to size op‑amp cores for a chopper instrumentation amplifier, uncovering defects that a sizing‑only baseline missed.

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AnalogScout is a new framework that uses a large language model (LLM) agent to perform PVT-aware analog device sizing more efficiently. By reusing elite designs, applying analytic gm/Id local sizing, and refining globally under full-corner verification, it cuts power consumption by 43.3% compared to the CMA-ES evolution strategy while using the same 16,000 simulations on three SKY130 OTAs. The method also outperforms Bayesian optimization and other LLM-driven flows on the tested OTAs.

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