RFChipAgent: Multi-Agentic AI Flow for Analog/RF Chip Design

Key points

• Analog/RF circuits remain the critical interface between digital computation and the physical world, and emerging standards from Wi-Fi 7 to 6G place stringent demands on them, yet analog/RF design remains one of the most labor-intensive steps in chip development.1
• Third, a closed-loop hybrid circuit-sizing engine combines Tree-structured Parzen Estimator (TPE) and CMA-ES optimization, evaluating every candidate in a simulator-in-the-loop framework.1
• We validate RFChipAgent on a family of GF22FDSOI 60 GHz wideband mm-wave low-noise amplifier (LNA) topologies, demonstrating automated topology generation, specification-driven design-space exploration, and simulator-guided optimization.1
• This work establishes a foundation for LLM-driven multi-agent electronic design automation (EDA) for analog/RF circuits.1

Sources (1)

• [1]RFChipAgent: Multi-Agentic AI Flow for Analog/RF Chip Design
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 08:02 PM

  We present RFChipAgent, a first-of-its-kind multi-agent flow of large language model (LLM) agents for end-to-end analog/RF circuit design automation, in which AI agents collaboratively orchestrate the complete design flow under human supervision.

  Analog/RF circuits remain the critical interface between digital computation and the physical world, and emerging standards from Wi-Fi 7 to 6G place stringent demands on them, yet analog/RF design remains one of the most labor-intensive steps in chip development.

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