Automated Design of Smart Nanoelectronic Sensors Using Generative Adversarial Networks

Arya Shafiei Sabet¹

Abstract :
Abstract: 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 [1, 2]. 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 [3, 4]. 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 [5, 6]. 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 [7, 8]. A systematic review of 200 journal articles and book chapters reveals that (a) specific, reproducible methods for sensor design optimization are rare (less than 30%) [9]; (b) design methods often fail to capture critical interdependencies among sensitivity, material properties, and energy efficiency-the very attributes they aim to enhance [10, 11]; (c) quantitative approaches dominate (approximately 70%) [12]; (d) social and industrial stakeholder engagement is minimal (less than 20%) [13]; and (e) most methods remain confined to disciplinary silos, with only 25% integrating cross-disciplinary tools and less than 15% combining quantitative and qualitative methodologies [14, 15]. 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 [16]. By evaluating existing methods against these features, we highlight 15 studies that demonstrate promising progress in automated design using Generative Adversarial Networks (GANs) [17]. 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 [18]. Simulations in COMSOL Multiphysics suggest GAN-based designs achieve up to 32% higher sensitivity and 60% reduced design time, offering a scalable path for next-generation sensors [19, 20].

Keywords : nanoelectronic sensors generative adversarial networks (gans) automated design nanowires sensitivity comsol multiphysics

---

زبان مقاله : انگلیسی
محور مقاله : مهندسی برق
محل انتشار : دومین کنگره بین المللی علوم مهندسی و توسعه شهری پایدار
آدرس وب سایت کنفرانس : dtuconf.com
کد IOI مقاله : AAAB-AABDZC
لینک مستقیم مقاله : https://isnac.ir/AAAB-AABDZC
نمایش چکیده فارسی : https://isnac.ir/SA/AAAB-AABDZC

سایر مقالات ارائه دهنده

1. Innovative applications of microchips in medicine, industry, and personal data management
2. Neural Networks and Deep Learning: Applications, Challenges, and the Future of Artificial Intelligence
3. Artificial Intelligence and Smart Circuits: A Step Towards Efficient and Sustainable Power Grids
4. Energy Optimization in Nanoelectronic Circuits Using Reinforcement Learning
5. Automated Design of Smart Nanoelectronic Sensors Using Generative Adversarial Networks

سایر مقالات محل انتشار

1. Advanced Wound Dressings Using Electrospinning Technology
2. ادبیات: هنر کلامی، معماری: هنر بی کلام
3. بررسی تاثیر زیبایی شناسی در معماری بر بهزیستی- روان شناختی ساکنین نمونه موردی: ساختمان‌ های مسکونی بلند مرتبه مشهد (مطالعه مروری)
4. بازگشت به ریشه‌ها: تلفیق سنت و مدرنیته در معماری مسکن برای ارتقای کیفیت زندگی
5. A review on Impacts of climate change on rangelands Soil surface water content, albedo, vegetation cover, vegetation distribution
6. طراحی مجتمع مسکونی با رویکرد معماری پایدار
7. Modified polyethylene terephthalate membrane for extraction of polycyclic aromatic hydrocarbons
8. ادغام فناوری های نوین با روش های سنتی معماری در مجموعه های مسکونی کاشان
9. Energy Optimization in Nanoelectronic Circuits Using Reinforcement Learning
10. Automated Design of Smart Nanoelectronic Sensors Using Generative Adversarial Networks
11. (معماری سبز و همزیستی با طبیعت، گامی به سوی پایداری (نمونه موردی: ساختمان شهرداری در سوئد))
12. A Review of Dust Storm Trends in Major Middle Eastern Cities and the Influence of Meteorological Parameters (2015–2025)
13. Proposing Climatic Design Strategies for Residential Buildings in Iran’s Hot and Humid Climate Using Climate Consultant Software: A Case Study of Chabahar City
14. تحلیل ویژگی و اصول معماری همساز با اقلیم ساختمان های مسکونی در اقلیم گرم و خشک ( مطالعه موردی شهر کاشان )
15. Analysis of Environmental Justice in the Development of Sustainable Urban Landscapes: A Case Study of Zanjan City, Iran