ADVANCING AI HARDWARE ARCHITECTURE: PERFORMANCE ANALYSIS OF CARBON-BASED SEMICONDUCTORS IN HIGH-PERFORMANCE COMPUTING APPLICATIONS

The advent of artificial intelligence has exposed fundamental limitations in traditional silicon-based semiconductor technologies, necessitating innovative solutions for next-generation computing architectures. This comprehensive article examines the potential of carbon-based semiconductors, particularly graphene and carbon nanotubes (CNTs), in addressing the challenges posed by modern AI workloads.These materials exhibit exceptional electrical, thermal, and mechanical properties that significantly surpass conventional silicon semiconductors, enabling faster processing speeds, reduced energy consumption, and enhanced miniaturization capabilities.The article explores the fundamental characteristics of carbon-based materials, their integration into existing semiconductor architectures, and the challenges involved in scaling production for commercial applications.Particular attention is given to the role