# Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization

arXiv: https://arxiv.org/abs/2204.12948 · HTML: https://mlsanity.com/paper/2204.12948/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals](https://mlsanity.com/paper/2201.12861/) (2022)
- [Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices](https://mlsanity.com/paper/1911.12815/) (2019)

## Built on by (most cited first, 10 of 10)

- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies](https://mlsanity.com/paper/2503.00205/) (2025)
- [AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models](https://mlsanity.com/paper/2412.19824/) (2024)
- [Schemato -- An LLM for Netlist-to-Schematic Conversion](https://mlsanity.com/paper/2411.13899/) (2024)
- [INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers](https://mlsanity.com/paper/2407.07346/) (2024)
- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation](https://mlsanity.com/paper/2606.15052/) (2026)
- [Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability](https://mlsanity.com/paper/2309.00738/) (2023)
- [KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs](https://mlsanity.com/paper/2410.22946/) (2024)
- [Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning](https://mlsanity.com/paper/2506.08029/) (2025)

## Abstract (arXiv)

The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a multimodal network, the method best learns the complex relations between circuit parameters and design targets, enabling optimal decisions in the optimization process. Experimental results on exemplary circuits show it achieves human-level design accuracy (99%) 1.5X efficiency of existing best-performing methods. Our method also shows better generalization ability to unseen specifications and optimality in circuit performance optimization. Moreover, it applies to design radio-frequency circuits on emerging semiconductor technologies, breaking the limitations of prior learning methods in designing conventional analog circuits.