Optoelectronic Neuromorphic Logic Using Quantum-Dot Cellular Automata (QCA) and Plasmonic Synapses: A Drude Model-Based Approach

This paper presents an optoelectronic neuromorphic logic framework by integrating plasmonic synapses with Quantum-Dot Cellular Automata (QCA) using a Drude model-based approach. Unlike prior studies, the work emphasizes frequency-dependent plasmonic-QCA coupling, crosstalk mitigation strategies, and material-dependent trade-offs. The proposed architecture demonstrates improved performance in terms of power consumption, latency, and device scalability compared to state-of-the-art designs. While the Drude model effectively captures the free-electron behavior of classical plasmonic materials (e.g., silver, gold), its limitations for nonDrude materials (e.g., graphene, ITO) are highlighted, with suggestions for extended modeling frameworks. This research provides both a theoretical foundation and a pathway toward scalable neuromorphic nanoelectronics.