An Unsupervised Learning-Based Multidimensional Scaling Approach for Placement and Routing of Monolithic Microwave Integrated Circuit

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Yifan Wu, Junchao Wang, Jiayu Chen, Yaqi Wang, Bin You, Jun Liu

Published: 01 Jan 2025, Last Modified: 01 Mar 2026IEEE Transactions on Computer-Aided Design of Integrated Circuits and SystemsEveryoneRevisionsCC BY-SA 4.0
Abstract: The layout method for Radio Frequency/Monolithic Microwave Integrated Circuit (RF/MMIC) is one of the key components in achieving MMIC design automation. Our work proposes a multi-stage progressive automated layout framework addressing MMIC layout challenges under 0.25 μm GaAs pHEMT technology. This framework employs dimensionality reduction from unsupervised learning, integrates practical layout design rules, and achieves automated layout through analytical optimization methods. Applied to multiple cases including filters, low-noise amplifiers (LNAs), and power amplifiers (PAs), the method successfully generated manufacturable layouts; the process achieves end-to-end automation from placement to routing, producing layouts that are verified to be DRC-clean. Simulation-verified results demonstrate comparable performance to manual designs while achieving ≥10× speedup over conventional methods. We believe this work contributes valuable attempts toward automated MMIC layout generation and advances progress in MMIC design automation.