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This paper proposes an AI-assisted methodology for the automated design and optimization of CMOS RF circuits, with primary emphasis on low-noise amplifiers (LNAs). The proposed framework integrates physics-informed neural networks (PINNs) for transistor modeling and reinforcement learning (RL) for circuit topology exploration. By combining data-driven intelligence with physical constraints, the model predicts device and circuit parameters directly from process conditions. A Pareto-optimized RL engine minimizes noise figure and power consumption while maximizing gain and bandwidth. Simulation results on a 130-nm CMOS process show more than 70% reduction in optimization time and <3% mean absolute error compared to SPICE simulations, outperforming existing rule-guided evolutionary approaches. The framework establishes a new direction for intelligent, data-driven analog/RF design automation.
"Automated Design and Optimization Framework for CMOS RF Circuits", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b486-b490, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511155.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator