Abstract

The analog layout design for the RF front-end circuits has remained a critical and time-intensive stage within the integrated circuit development cycle. Conventional manual methodologies have relied heavily on expert knowledge, iterative tuning, and heuristic rules, which has limited scalability under advanced technology nodes. The increasing complexity of multi-band and high-frequency RF front- ends has demanded automated strategies that have preserved performance while reducing design effort. Traditional electronic design automation tools have struggled to generalize across diverse RF blocks, which has resulted in suboptimal trade-offs between gain, noise, linearity, and area. Layout-dependent effects such as parasitic coupling and mismatch have further complicated early-stage optimization. These challenges have motivated the need for a data- driven synthesis framework that has adapted to process variability and design constraints. This work has presented a machine-learning- assisted analog layout synthesis framework for RF front-end circuits. A supervised learning model has learned geometric and topological layout patterns from annotated analog layouts that have captured performance-sensitive features. A reinforcement learning agent has refined placement and routing decisions that which has considered electromagnetic constraints, symmetry, and matching rules. The proposed pipeline has integrated circuit simulation feedback that has guided iterative layout refinement under process corners. Experimental evaluation on low-noise amplifiers and mixers demonstrates that the synthesized layouts achieve gain up to 13.4 dB, noise figure as low as 1.4 dB, linearity of -17.0 dBm, layout area of 1165 µm², and parasitic capacitance of 20 fF, outperforming existing template-based, optimization-driven, and reinforcement learning placement methods. The proposed method reduces layout generation time by over 60% while maintaining consistent performance across transistor widths (0.16–0.24 µm) and lengths (0.32–0.36 µm), indicating strong generalization and suitability for next-generation RF front-end designs.

Authors

A. Muthumari, P. Uma Maheswari
Anna University Regional Campus Madurai, India

Keywords

Analog Layout Synthesis, RF Front-End Design, Machine Learning, Electronic Design Automation, Parasitic Optimization