FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909872422912000 |
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| author | Mehradfar, Asal Zhao, Xuzhe Huang, Yilun Ceyani, Emir Yang, Yankai Han, Shihao Aghasi, Hamidreza Avestimehr, Salman |
| author_facet | Mehradfar, Asal Zhao, Xuzhe Huang, Yilun Ceyani, Emir Yang, Yankai Han, Shihao Aghasi, Hamidreza Avestimehr, Salman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design Mehradfar, Asal Zhao, Xuzhe Huang, Yilun Ceyani, Emir Yang, Yankai Han, Shihao Aghasi, Hamidreza Avestimehr, Salman Machine Learning Artificial Intelligence Hardware Architecture Computational Engineering, Finance, and Science 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. |
| title | FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design |
| topic | Machine Learning Artificial Intelligence Hardware Architecture Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2505.21923 |