# AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

arXiv: https://arxiv.org/abs/2001.01808 · HTML: https://mlsanity.com/paper/2001.01808/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2020.

## Built on by (most cited first, 30 of 51)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)
- [DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks](https://mlsanity.com/paper/2110.00211/) (2021)
- [AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs](https://mlsanity.com/paper/2508.02518/) (2025)
- [Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design](https://mlsanity.com/paper/2203.15913/) (2022)
- [EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2509.25510/) (2025)
- [Schemato -- An LLM for Netlist-to-Schematic Conversion](https://mlsanity.com/paper/2411.13899/) (2024)
- [LLM-USO: Large Language Model-based Universal Sizing Optimizer](https://mlsanity.com/paper/2502.02764/) (2025)
- [AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing](https://mlsanity.com/paper/2511.03697/) (2025)
- [Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization](https://mlsanity.com/paper/2204.12948/) (2022)
- [TAG: Learning Circuit Spatial Embedding From Layouts](https://mlsanity.com/paper/2209.03465/) (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)
- [INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers](https://mlsanity.com/paper/2407.07346/) (2024)
- [MuaLLM: A Multimodal Large Language Model Agent for Circuit Design Assistance with Hybrid Contextual Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.08137/) (2025)
- [Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.13185/) (2022)
- [LLM-based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2504.11497/) (2025)
- [RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL](https://mlsanity.com/paper/2207.06412/) (2022)
- [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)
- [EasySize: Elastic Analog Circuit Sizing via LLM-Guided Heuristic Search](https://mlsanity.com/paper/2508.05113/) (2025)
- [HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization](https://mlsanity.com/paper/2511.19669/) (2025)
- [AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents](https://mlsanity.com/paper/2602.02849/) (2026)
- [AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents](https://mlsanity.com/paper/2603.23910/) (2026)
- [GraCo -- A Graph Composer for Integrated Circuits](https://mlsanity.com/paper/2411.13890/) (2024)
- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddings](https://mlsanity.com/paper/2511.07658/) (2025)
- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)
- [Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks](https://mlsanity.com/paper/2311.17073/) (2023)

## Abstract (arXiv)

Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs). While digital subsystems have design flows that are conducive to rapid iterations from specification to layout, analog and mixed-signal modules face the challenge of a long human-in-the-middle iteration loop that requires expert intuition to verify that post-layout circuit parameters meet the original design specification. Existing automated solutions that optimize circuit parameters for a given target design specification have limitations of being schematic-only, inaccurate, sample-inefficient or not generalizable. This work presents AutoCkt, a machine learning optimization framework trained using deep reinforcement learning that not only finds post-layout circuit parameters for a given target specification, but also gains knowledge about the entire design space through a sparse subsampling technique. Our results show that for multiple circuit topologies, AutoCkt is able to converge and meet all target specifications on at least 96.3% of tested design goals in schematic simulation, on average 40X faster than a traditional genetic algorithm. Using the Berkeley Analog Generator, AutoCkt is able to design 40 LVS passed operational amplifiers in 68 hours, 9.6X faster than the state-of-the-art when considering layout parasitics.