Energy Optimization in Nanoelectronic Circuits Using Reinforcement Learning

Nanoelectronic sensors are vital for innovative uses like medical diagnostics, environmental monitoring, and the Internet of Things (IoT), though their creation faces major challenges stemming from nanoscale complexities, quantum effects, and the need for high precision [۱], [۲]. This study presents a novel approach using Generative Adversarial Networks (GANs) to simplify and improve the design process of nanoelectronic sensors, aiming to boost sensitivity, energy efficiency, and scalability [۳], [۴]. The GAN setup, consisting of a Generator and a Discriminator, was developed using a collection of ۲۰۰۰ sensor designs, incorporating both nanowire- and graphene-based setups, to craft new layouts with limited human input [۵], [۶]. Using COMSOL Multiphysics for simulations, the sensors designed by the GAN showed a sensitivity increase of up to ۳۲% (average: ۸۲ nS/mM) and a ۳۰% reduction in energy use (average: ۸.۵ µW) when compared to manually crafted designs [۷], [۸]. Furthermore, this method cut down the design duration by ۶۰%, shrinking the process from weeks to less than ۴۸ hours, thus providing a practical option for industrial use [۹], [۱۰]. The success of this approach lies in the GAN’s capability to navigate expansive design possibilities and produce unique configurations, like multilayered nanowires, which conventional techniques cannot achieve [۱۱], [۱۲]. This strategy tackles the issue of limited data in nanoelectronics while establishing a fresh standard for eco-friendly sensor development, potentially transforming fields like healthcare, environmental care, and smart technology systems [۱], [۳]. The research highlights the game-changing potential of GANs in nanoelectronic design, laying a foundation for future breakthroughs in nanotechnology [۴], [۵].

کلیدواژه ها:

Nanoelectronic Sensors ، Generative Adversarial Networks (GANs) ، Automated Design ، Sensitivity Optimization ، Nanowires ، COMSOL Multiphysics