Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights

TL;DR

This paper evaluates supervised machine learning models for analog and RF circuit design, demonstrating their effectiveness in reducing errors and improving scalability across various circuit types and complexities.

Contribution

It provides a comprehensive benchmark of ML models for circuit design, highlighting model performances and scalability insights for different circuit complexities.

Findings

Transformers excel in modeling non-linear relationships.

Simple circuits achieve mean relative errors as low as 0.3%.

Heterogeneous circuits see an 88% error reduction with more training data.

Abstract

Automating analog and radio-frequency (RF) circuit design using machine learning (ML) significantly reduces the time and effort required for parameter optimization. This study explores supervised ML-based approaches for designing circuit parameters from performance specifications across various circuit types, including homogeneous and heterogeneous designs. By evaluating diverse ML models, from neural networks like transformers to traditional methods like random forests, we identify the best-performing models for each circuit. Our results show that simpler circuits, such as low-noise amplifiers, achieve exceptional accuracy with mean relative errors as low as 0.3% due to their linear parameter-performance relationships. In contrast, complex circuits, like power amplifiers and voltage-controlled oscillators, present challenges due to their non-linear interactions and larger design spaces. For heterogeneous circuits, our approach achieves an 88% reduction in errors with increased training data, with the receiver achieving a mean relative error as low as 0.23%, showcasing the scalability and accuracy of the proposed methodology. Additionally, we provide insights into model strengths, with transformers excelling in capturing non-linear mappings and k-nearest neighbors performing robustly in moderately linear parameter spaces, especially in heterogeneous circuits with larger datasets. This work establishes a foundation for extending ML-driven design automation, enabling more efficient and scalable circuit design workflows.