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Volume 18, Issue 10
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973
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Abstract

Background

Reducing power consumption in digital circuits can be achieved by minimizing the number of transitions, and Gray code provides a binary numeral system optimized for this purpose. Traditional CMOS-based counters face limitations in power efficiency and performance at nanoscale levels. This research presents a novel design of a Gray code counter utilizing Carbon Nanotube Field-Effect Transistors (CNTFETs) as a high-performance alternative to CMOS technology.

Methods

The CNTFET-based Gray code counter was evaluated across a range of temperatures (25°C to 100°C), input voltages (0.7V to 1.3V), and clock frequencies (200 MHz to 800 MHz). Supervised machine learning was employed to predict and analyze key performance metrics, including propagation delay, power consumption, and Power-Delay Product (PDP), for both CMOS and CNTFET Gray code counters under varying conditions.

Results

The results demonstrate that the CNTFET-based Gray code counter exhibits significantly lower power dissipation, faster operation, and a minimum PDP compared to its CMOS counterpart across the tested temperature, voltage, and frequency variations. The machine learning predictions aligned closely with simulation results, confirming the accuracy of this approach in optimizing the design.

Conclusion

The study validates the CNTFET Gray code counter as a highly efficient, low-power solution suited for high-performance applications. Its superior performance characteristics suggest that CNTFET technology, coupled with AI-driven optimization, holds promise for advanced low-power VLSI circuit designs.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-02-13
2026-01-02

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Power Efficient Counter Design using CNTFET with AI Integration
Recent Advances in Electrical & Electronic Engineering 18, 2069 (2025); https://doi.org/10.2174/0123520965358804241209095031
/content/journals/raeeng/10.2174/0123520965358804241209095031
/content/journals/raeeng/10.2174/0123520965358804241209095031
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  • Article Type:     Research Article
Keyword(s): artificial intelligence; CMOS; CNTFET; Flip-Flop; linear regression; Machine learning; nano-scaled devices; synchronous counter

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