Graphene Nanoribbon-FET for Higher Drive Current using Machine Learning-Enhanced First Principles Analysis-Scilight

Electrical Engineering and Technology

Research article

Graphene Nanoribbon-FET for Higher Drive Current using Machine Learning-Enhanced First Principles Analysis

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https://doi.org/10.54963/eet.v1i1.1283
Singh , A. P., Katta , S. S., Baghel , R. K., & Yadav, S. (2025). Graphene Nanoribbon-FET for Higher Drive Current using Machine Learning-Enhanced First Principles Analysis. Electrical Engineering and Technology, 1(1), 1–14. https://doi.org/10.54963/eet.v1i1.1283
Volume 1 Issue 1 (2025)
Copyright (c) 2025 Abhay Pratap Singh , Sai Shirov Katta , R. K. Baghel , Shailendra Yadav
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors

  • Abhay Pratap Singh

    Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India
  • Sai Shirov Katta

    Department of Electrical Engineering, Indian Institute of Technology, Patna 801106, India
  • R. K. Baghel

    Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India
  • Shailendra Yadav

    Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India

Received: 1 April 2025; Revised: 17 May 2025; Accepted: 25 May 2025; Published: 2 June 2025

This work presents a novel and innovative design approach for Graphene Nano-Ribbon Field Effect Transistors (GNR FETs), uniquely employing Zigzag Graphene Nano-Ribbons (ZGNRs) as electrodes and Armchair Graphene Nano-Ribbons (AGNRs) as the channel region. To deeply understand device performance, rigorous first-principles modeling was conducted, leveraging Extended-Hückel formalism alongside Landauer-Buttiker transport theory. Extensive Technology Computer-Aided Design (TCAD) simulations systematically explored the impact of critical parameters such as doping concentration (ND), gate voltage (Vg), and drain voltage (Vd) on transistor behavior. However, the computational intensity associated with such comprehensive analyses necessitated the introduction of an advanced Machine Learning (ML)-assisted methodology, specifically employing a Conventional Artificial Neural Network (C-ANN). Remarkably, this ML-driven strategy achieved highly accurate results within significantly reduced computational times of just 80–90 seconds, underscoring its practicality and efficiency. Furthermore, the intrinsic 2.71 eV band gap of the pristine AGNR channel was effectively modulated in a broad range (0.013–1.6 eV) through controlled doping and engineered defects. An N-passivated AGNR FET demonstrated an extraordinary 157 times enhancement in drive current, although its negligible band gap raised concerns regarding leakage currents. Alternatively, the N-doped Stone-Wales AGNR FET provided a well-balanced performance with a 33.21 nA drive current and a suitable 0.58 eV band gap, substantially reducing leakage risks, enhancing thermal stability, and improving peak inverse voltage robustness. This pioneering ML-assisted C-ANN approach highlights significant potential for accelerating accurate and reliable nano-transistor analyses.

Keywords:

Graphene Nanoribbons (GNR) Stone-Wales (SW) Defect Non-Equilibrium Green’s Function (NEFG) First Principle Modeling Machine Learning (ML)

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