Abstract Detail

Abstract

The rapid evolution of nanoelectronics, driven by the ongoing miniaturization of electronic components and the increasing complexity of nanoscale systems, has ushered in new challenges in materials discovery, device fabrication, and performance optimization. Traditional approaches based on physics-driven modeling and experimental trial-and-error are proving increasingly inadequate due to the enormous design space, nonlinear behavior, and data-intensive nature of nanoscale phenomena. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools that offer new paradigms for tackling these challenges.

This paper explores the integration of AI and ML techniques in the field of nanoelectronics, with a particular focus on their applications in materials design and nanoscale device optimization. The convergence of these domains is leading to the development of “smart nanoelectronics”, systems that are not only physically compact and energy-efficient but also capable of self-learning, adaptation, and intelligent decision-making during both design and operation.

In materials design, ML algorithms are increasingly used to predict material properties, identify promising nanomaterials, and accelerate the discovery process through high-throughput computational screening. Deep learning and generative models such as variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) have enabled the inverse design of nanomaterials with tailored properties, such as high carrier mobility, thermal conductivity, or flexibility. Reinforcement learning, on the other hand, is finding applications in experimental design, guiding the synthesis of nanostructures with minimal human intervention.

In nanoscale device engineering, AI is being leveraged to optimize device architectures, simulate quantum-level interactions, and predict performance degradation mechanisms. Neural networks and surrogate models reduce the computational burden of multi-physics simulations, enabling faster design iterations and real-time predictive maintenance. Additionally, ML-driven fault detection and classification systems improve reliability in nanoscale transistors, sensors, and memory elements by identifying defects and process anomalies at sub-nanometer resolutions.

The paper also reviews case studies where AI/ML models have successfully accelerated innovation in fields such as nano-biosensors, neuromorphic computing, and energy-efficient nano-devices. Emphasis is placed on interdisciplinary collaboration where insights from materials science, data science, and electrical engineering converge to create intelligent systems with capabilities beyond the reach of conventional electronics.

Despite these advancements, several challenges persist. These include limited availability of high-quality datasets, lack of interpretability in black-box models, difficulties in generalizing models across different material systems, and integration constraints with existing fabrication workflows. Addressing these challenges requires the development of hybrid models that combine domain knowledge with data-driven learning, standardized data repositories, and improved explainability techniques.

In conclusion, the fusion of AI and ML with nanoelectronics represents a fundamental shift in how we approach the design and realization of next-generation electronic systems. By enabling more intelligent, adaptable, and efficient nanoscale devices, this integration stands to significantly impact a wide range of applications—from wearable electronics and biomedical diagnostics to quantum computing and sustainable energy systems. This paper aims to provide a holistic understanding of this emerging field, highlighting key methodologies, breakthroughs, and the road ahead for smart nanoelectronics.

What will the audience take away from your presentation? 

1. Understanding the Limitations of Traditional Nanoelectronics Design

Why classical physics-based modeling and trial-and-error experiments struggle with the complexity, scale, and data intensity of modern nanoelectronics.

Recognition of the need for new computational paradigms to handle vast design spaces and nonlinear nanoscale phenomena.

2. Role of AI and ML in Accelerating Materials Discovery

How machine learning algorithms (like deep learning, VAEs, GANs) are revolutionizing the prediction of material properties and the inverse design of nanomaterials.

Insight into the use of reinforcement learning to optimize experimental design and nanostructure synthesis, reducing human intervention and speeding up discovery.

3. AI-driven Nanoscale Device Optimization

Application of AI in optimizing device architectures, simulating quantum effects, and forecasting device degradation.

Use of neural networks and surrogate models to make complex multi-physics simulations more computationally efficient.

Implementation of ML models for fault detection and reliability enhancement in nanoscale transistors and sensors.

4. Case Studies Highlighting AI/ML Impact

Real-world examples where AI/ML have accelerated innovation in nano-biosensors, neuromorphic computing, and energy-efficient devices.

Importance of interdisciplinary collaboration combining materials science, data science, and electrical engineering.

5. Challenges and Future Directions in Smart Nanoelectronics

Awareness of existing challenges: data scarcity, black-box model interpretability, model generalizability, and integration with fabrication workflows.

The future need for hybrid models combining domain expertise and data-driven learning, standardized datasets, and explainability techniques to advance the field.

These points will give our audience a clear picture of both the transformative potential and the practical hurdles of using AI and ML in nanoelectronics, preparing them for a rapidly evolving research landscape.

Biography

Dr. Yashu Swami is a Professor in the Department of ECE at Manav Rachna International Institute of Research and Studies, Faridabad, India and a Postdoctoral Researcher at the EE Department, IIT Ropar. With a Ph.D. in Nanoelectronics (MNNIT Allahabad, India). He brings over 15 years of academic, research, and industry experience. His expertise spans VLSI, AI integration, and nanoelectronics. Known for leadership, adaptability, and innovative thinking, Dr. Swami is passionate about interdisciplinary collaboration, academic mentorship, and contributing meaningfully to technological advancement.