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

2025-01-21

AI Summary

Low design efficiency and high complexity in analog/RF circuit design hinder rapid prototyping and optimization. Method: We propose a supervised-learning-based parameter-to-performance direct mapping framework applicable to both homogeneous and heterogeneous circuits—including LNAs, PAs, VCOs, and receivers—and systematically evaluate Transformer, random forest, k-NN, and fully connected networks for cross-circuit generalization. We introduce a relative-error-based normalization scheme for fair performance assessment and develop a scalable error-suppression strategy tailored to heterogeneous circuits. Contributions/Results: We reveal that parameter–performance linearity critically governs model selection; achieve mean relative errors of 0.3% for LNAs and 0.23% for receivers; reduce prediction error by 88% via heterogeneous data augmentation; and demonstrate that Transformers excel in strongly nonlinear regimes, whereas k-NN exhibits superior robustness under moderate linearity.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Machine Learning
• Circuit Design
• Supervised Learning

Innovation

• Supervised Learning
• Circuit Design
• Machine Learning Models