Memristive and memsensor devices for filament free sparse CNT networks

我们在水平和垂直忆阻器件上的新的实验和理论结果允许基本的理解，也揭示了新的应用可能性。实验和计算机模拟强烈表明，与文献结果相反，Ag离子迁移率的限制（例如，通过从纯Ag到AgAu合金纳米颗粒的转变）对于允许忆阻器件的无丝切换是必要的。现在，基于定义的尺寸和组成的金属团簇沉积的良好表征的设置，我们计划进一步深入了解绝缘基质内的非介电Ag离子传输，特别是通过原位研究，并实现用于稀疏网络应用的可靠忆阻器件。重点将放在非氧化基质上，以排除氧缺陷的叠加效应。除了原位表征的优势外，水平几何结构还将用于新的memsensor应用，这是一种已经成功演示的忆阻器和传感器的组合。为了实现完整的电路，我们将设计和研究用于以下几个目的的稀疏CNT网络：a）将忆阻金属氧化物传感器（memsensors）与简单的神经网络联系起来，B）通过随后的AgAu簇沉积直接形成专用的神经网络，c）作为与网络外围联系的忆阻器件的互连层。最终的目标将是一个独立的决策神经网络响应非人工输入信号。

Our novel experimental and theoretical results on horizontal and vertical memristive devices allow fundamental understanding and also reveal new application possibilities. Experiments and computer simulation strongly suggest that, in contrast to literature results, a limitation of Ag ion mobility (e.g. by transition from pure Ag to AgAu-alloy nanoparticles) is necessary to allow for filament free switching of memristive devices. Now, based on a well-characterized setup for metal cluster deposition of defined size and composition, we plan to get further insight into non-filamentary Ag ion transport within insulating matrixes, especially by in-situ studies, and to realize reliable memristive devices for sparse network applications. The focus will be put on non-oxidic matrices to rule out superimposed effects from oxygen defects. Beside the advantages for in-situ characterization, the horizontal geometry will be employed for a new memsensor application, a combination of memristors and sensors that could already be successfully demonstrated. To realize full circuits, we will design and investigate sparse CNT networks for several purposes: a) to contact memristive metal-oxide-sensors (memsensors) to simple neural networks, b) to form dedicated neural networks directly  by subsequent AgAu-cluster deposition, c) as an interconnection layer for memristive devices contacting the periphery of the network. The final goal will be a standalone decision making neural network responding to non-artificial input signals.