mlsanityData to 14 Oct 2026

PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation

arXiv 2606.15052 ↗First posted 13 Jun 2026cs.AR cs.AIcited by 2

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Abstract (arXiv)

Traditional design of analog circuits heavily relies on manual interventions across topology, sizing, and layout, with prior automation addressing stages in isolation. In this work, we propose PANDA, an LLM-enhanced framework that bridges high-level design intent to final layout by actively managing cross-stage dependencies through guided topology synthesis, substructure-aware sizing, and constraint-driven layout generation. This shifts automation from algorithm-centric execution to intent-centric co-design, reducing turnaround time from days or weeks to hours while improving design performance.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 46 references that are in the map. First posted on arXiv 13 Jun 2026.

Builds on

8 of 46 references.

References · 8

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  4. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  5. 2024AnalogCoder: Analog Circuit Design via Training-Free Code Generationcited by 84
  6. 2024LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Designcited by 25
  7. 2022Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimizationcited by 10
  8. 2023Post-Layout Simulation Driven Analog Circuit Sizingcited by 5

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Citing papers per year: 2026: 2.

2 papers.