AI-driven co-optimization of ONOFIC circuits and multiband antennas for low-power VLSI

Ramavathu Ramesh Naik (Chaitanya Deemed to be University)
Donapati Ramakrishna Reddy (Chaitanya Deemed to be University)
Krishnanaik Vankdoth (Chaitanya Deemed to be University)

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol 15, No 2: July 2026

This paper presents an artificial intelligence (AI)-assisted optimization framework for on-off current feedback controlled (ONOFIC)-enhanced domino circuits implemented in advanced fin field-effect transistor (FinFET) and carbon nanotube field-effect transistor (CNTFET) technologies. The framework integrates artificial neural network (ANN) surrogate modeling with evolutionary optimization (genetic algorithm (GA), particle swarm optimization (PSO), and NSGA-II) to reduce leakage, improve energy efficiency, and enhance robustness under process voltage temperature (PVT) variations, aging effects (bias temperature instability (BTI)/hot carrier injection (HCI)), and antenna-induced parasitic coupling. By replacing repeated HSPICE simulations with fast ANN predictions, the proposed methodology reduces computational cost by more than 90% while achieving up to 30–35% gains in leakage and power-delay product (PDP)/energy-delay product (EDP) performance. The results demonstrate that ANN-assisted evolutionary optimization provides a scalable and technology-agnostic workflow suitable for next-generation internet of thing (IoT), radio frequency (RF)-integrated, and low-power very large scale integration (VLSI) platforms.