Automated Design of Smart Nanoelectronic Sensors Using Generative Adversarial Networks

The design of smart nanoelectronic sensors is critical for addressing complex challenges in healthcare, environmental monitoring, and the Internet of Things (IoT), yet conventional approaches struggle to balance performance, scalability, and design efficiency [۱, ۲]. The nanoelectronic sensor design paradigm seeks to optimize sensitivity, response time, and power consumption, while internalizing quantum effects and material complexities, to guide the development of high-performance, sustainable sensors [۳, ۴]. However, while this paradigm offers a promising conceptual framework, the use of systematic methods to evaluate sensor performance or support scalable, industry-relevant designs remains limited [۵, ۶]. This paper reviews methods for nanoelectronic sensor design to establish a knowledge base of existing approaches and promote the development of analytical tools aligned with automated design principles [۷, ۸]. A systematic review of ۲۰۰ journal articles and book chapters reveals that (a) specific, reproducible methods for sensor design optimization are rare (less than ۳۰%) [۹]; (b) design methods often fail to capture critical interdependencies among sensitivity, material properties, and energy efficiency-the very attributes they aim to enhance [۱۰, ۱۱]; (c) quantitative approaches dominate (approximately ۷۰%) [۱۲]; (d) social and industrial stakeholder engagement is minimal (less than ۲۰%) [۱۳]; and (e) most methods remain confined to disciplinary silos, with only ۲۵% integrating cross-disciplinary tools and less than ۱۵% combining quantitative and qualitative methodologies [۱۴, ۱۵]. To address these gaps, we derive four key features of nanoelectronic sensor design tools-innovation, scalability, collaboration, and implementation-from the literature to guide future advancements [۱۶]. By evaluating existing methods against these features, we highlight ۱۵ studies that demonstrate promising progress in automated design using Generative Adversarial Networks (GANs) [۱۷]. This paper finds that to tackle complex design challenges, transdisciplinary approaches are essential, incorporating material science, artificial intelligence, and industrial perspectives; leveraging mixed-methods tools; and engaging stakeholders to ensure practical implementation [۱۸]. Simulations in COMSOL Multiphysics suggest GAN-based designs achieve up to ۳۲% higher sensitivity and ۶۰% reduced design time, offering a scalable path for next-generation sensors [۱۹, ۲۰].

کلیدواژه ها:

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