# FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

arXiv: https://arxiv.org/abs/2505.21923 · HTML: https://mlsanity.com/paper/2505.21923/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 16 of 16)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits](https://mlsanity.com/paper/2407.18269/) (2024)
- [LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Design](https://mlsanity.com/paper/2406.18873/) (2024)
- [Learning to Design Circuits](https://mlsanity.com/paper/1812.02734/) (2018)
- [Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design](https://mlsanity.com/paper/2203.15913/) (2022)
- [Learning to Design Analog Circuits to Meet Threshold Specifications](https://mlsanity.com/paper/2307.13861/) (2023)
- [AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design](https://mlsanity.com/paper/2407.18272/) (2024)
- [AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesis](https://mlsanity.com/paper/2409.08534/) (2024)
- [ALIGN: A System for Automating Analog Layout](https://mlsanity.com/paper/2008.10682/) (2020)
- [Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.13185/) (2022)
- [Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights](https://mlsanity.com/paper/2501.11839/) (2025)

## Built on by (most cited first, 5 of 5)

- [ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning](https://mlsanity.com/paper/2603.29068/) (2026)
- [RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction](https://mlsanity.com/paper/2508.16403/) (2025)
- [LLMs for Analog Circuit Design Continuum (ACDC)](https://mlsanity.com/paper/2512.09199/) (2025)
- [EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits](https://mlsanity.com/paper/2602.11461/) (2026)
- [CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis](https://mlsanity.com/paper/2610.19508/) (2026)

## Abstract (arXiv)

Designing analog circuits from performance specifications is a complex, multi-stage process encompassing topology selection, parameter inference, and layout feasibility. We introduce FALCON, a unified machine learning framework that enables fully automated, specification-driven analog circuit synthesis through topology selection and layout-constrained optimization. Given a target performance, FALCON first selects an appropriate circuit topology using a performance-driven classifier guided by human design heuristics. Next, it employs a custom, edge-centric graph neural network trained to map circuit topology and parameters to performance, enabling gradient-based parameter inference through the learned forward model. This inference is guided by a differentiable layout cost, derived from analytical equations capturing parasitic and frequency-dependent effects, and constrained by design rules. We train and evaluate FALCON on a large-scale custom dataset of 1M analog mm-wave circuits, generated and simulated using Cadence Spectre across 20 expert-designed topologies. Through this evaluation, FALCON demonstrates >99% accuracy in topology inference, <10% relative error in performance prediction, and efficient layout-aware design that completes in under 1 second per instance. Together, these results position FALCON as a practical and extensible foundation model for end-to-end analog circuit design automation.