Resistive Switching in Nanoparticle-Based Nanocomposites.

Abstract

The recent rapid progress in artificial intelligence (AI) and the processing of big data imposes a strong demand to explore novel approaches for robust and efficient hardware solutions. Neuromorphic engineering and brain-inspired electronics take inspiration from biological information pathways in neural assemblies, particularly their fundamental building blocks and organizational principles. In contrast, resistive switching in memristive devices is widely considered an electronic synapse with potential applications in in-memory computing and vector-matrix multiplication. Further aspects of brain-inspired electronics require exploring both organizational principles from individual building units towards connected networks, as well as the resistive switching properties of each unit. In this context, nanogranular matter made of nano-objects, such as nanoparticles or nanowires, has gained considerable research interest due to emergent brain-like, scale-free switching dynamics originating from the self-organization of its building units into connected networks. In this study, we review resistive switching in nanogranular matter featuring metal nanoparticles as their functional building blocks. First, common deposition strategies for nanoparticles, as well as nanoparticle-based nanocomposites, are discussed, and challenges in the investigation of their inherited resistive switching properties are addressed. Secondly, an overview of resistive switching properties in nanogranular matter, ranging from individual nanoparticles over sparse nanoparticle arrangements to highly interconnected nanogranular networks, is provided. Finally, concepts and examples of information processing using nanoparticle networks are outlined.