# Learning to Design Analog Circuits to Meet Threshold Specifications

arXiv: https://arxiv.org/abs/2307.13861 · HTML: https://mlsanity.com/paper/2307.13861/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [Learning to Design Circuits](https://mlsanity.com/paper/1812.02734/) (2018)

## Built on by (most cited first, 9 of 9)

- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design](https://mlsanity.com/paper/2407.18272/) (2024)
- [Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights](https://mlsanity.com/paper/2501.11839/) (2025)
- [FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design](https://mlsanity.com/paper/2505.21923/) (2025)
- [Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning](https://mlsanity.com/paper/2402.14236/) (2024)
- [PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters](https://mlsanity.com/paper/2601.21984/) (2026)
- [Graph Neural Networks Based Analog Circuit Link Prediction](https://mlsanity.com/paper/2504.10240/) (2025)
- [EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits](https://mlsanity.com/paper/2602.11461/) (2026)
- [Agent-Orchestration in Autonomous Chip Design](https://mlsanity.com/paper/2608.14035/) (2026)

## Abstract (arXiv)

Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as an alternative to manual expert design. It is straightforward for a design agent to learn an inverse function from desired performance metrics to circuit parameters. However, it is more common for a user to have threshold performance criteria rather than an exact target vector of feasible performance measures. In this work, we propose a method for generating from simulation data a dataset on which a system can be trained via supervised learning to design circuits to meet threshold specifications. We moreover perform the to-date most extensive evaluation of automated analog circuit design, including experimenting in a significantly more diverse set of circuits than in prior work, covering linear, nonlinear, and autonomous circuit configurations, and show that our method consistently reaches success rate better than 90% at 5% error margin, while also improving data efficiency by upward of an order of magnitude. A demo of this system is available at circuits.streamlit.app