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

2026-06-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Hardware Architecture & Design · Depth: Expert, quick

Summary

PANDA is an LLM-enhanced framework designed to automate analog circuit design, bridging high-level design intent to final layout generation. Traditionally, analog circuit design involves extensive manual intervention across topology, sizing, and layout stages, with existing automation often addressing these stages in isolation. PANDA tackles this by actively managing cross-stage dependencies through guided topology synthesis, substructure-aware sizing, and constraint-driven layout generation. This innovative approach shifts the design paradigm from algorithm-centric execution to intent-centric co-design. The framework significantly reduces design turnaround time from days or weeks to mere hours, while simultaneously improving overall design performance.

Key takeaway

For analog circuit designers aiming to accelerate development cycles, PANDA offers a critical shift. You should explore integrating LLM-enhanced frameworks to move from isolated, manual design steps to an intent-centric co-design approach. This can drastically cut your turnaround time from weeks to hours, allowing you to focus on higher-level architectural decisions and achieve superior design performance.

Key insights

PANDA uses LLMs to integrate analog circuit design stages, automating intent-to-layout with significant time and performance gains.

Principles

• Bridging design stages improves automation.
• Intent-centric co-design outperforms algorithm-centric.
• Managing cross-stage dependencies is key.

Method

PANDA employs guided topology synthesis, substructure-aware sizing, and constraint-driven layout generation, all enhanced by LLMs to manage cross-stage dependencies.

In practice

• Automate analog circuit layout.
• Reduce design cycle time.
• Improve circuit performance.

Topics

• Analog Circuit Design
• LLM Applications
• Hardware Architecture
• Design Automation
• Layout Generation
• Topology Synthesis

Best for: Research Scientist, AI Scientist, AI Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.