Memristors have gained considerable attention as key components in neuromorphic computing systems due to their ability to emulate synaptic behavior (Potentiation/Depression). Fe3O4, an iron oxide material, exhibits intriguing electrical properties, making it a promising candidate for memristor applications. This study focuses on the microwave-assisted synthesis of Fe3O4-based memristors and explores their potential for integration into neuromorphic systems. The utilization of microwave irradiation during fabrication enhances the control over the material's crystal structure, morphology, and electrical properties, leading to improved memristor performance. The characterization of the synthesized memristors reveals their memristive behavior, including analog resistive switching (ARS), and ultra-low energy consumption. Furthermore, the implementation of memristors in a neuromorphic system demonstrates their capability to emulate synapses, enabling the development of energy-efficient artificial neural activity −0.6 V/+ 0.6 V at read voltage 0.1 V (Potentiation/Depression). This research contributes to the advancement of Fe3O4-based memristors and their integration into state-of-the-art brain-inspired neuromorphic computing architectures.

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Microwave-Assisted Fe3O4-Based Memristor for Brain-Inspired Computing

  • Vivek Pratap Singh,
  • Chandra Prakash Singh,
  • Harsh Ranjan,
  • Saurabh Kumar Pandey

摘要

Memristors have gained considerable attention as key components in neuromorphic computing systems due to their ability to emulate synaptic behavior (Potentiation/Depression). Fe3O4, an iron oxide material, exhibits intriguing electrical properties, making it a promising candidate for memristor applications. This study focuses on the microwave-assisted synthesis of Fe3O4-based memristors and explores their potential for integration into neuromorphic systems. The utilization of microwave irradiation during fabrication enhances the control over the material's crystal structure, morphology, and electrical properties, leading to improved memristor performance. The characterization of the synthesized memristors reveals their memristive behavior, including analog resistive switching (ARS), and ultra-low energy consumption. Furthermore, the implementation of memristors in a neuromorphic system demonstrates their capability to emulate synapses, enabling the development of energy-efficient artificial neural activity −0.6 V/+ 0.6 V at read voltage 0.1 V (Potentiation/Depression). This research contributes to the advancement of Fe3O4-based memristors and their integration into state-of-the-art brain-inspired neuromorphic computing architectures.