Committee machines-a universal method to deal with non-idealities in memristor-based neural networks.

Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science-committee machines-in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.

Intrinsic Resistance Switching in Amorphous Silicon Suboxides: The Role of Columnar Microstructure.

We studied intrinsic resistance switching behaviour in sputter-deposited amorphous silicon suboxide (a-SiO x ) films with varying degrees of roughness at the oxide-electrode interface. By combining electrical probing measurements, atomic force microscopy (AFM), and scanning transmission electron microscopy (STEM), we observe that devices with rougher oxide-electrode interfaces exhibit lower electroforming voltages and more reliable switching behaviour. We show that rougher interfaces are consistent with enhanced columnar microstructure in the oxide layer. Our results suggest that columnar microstructure in the oxide will be a key factor to consider for the optimization of future SiOx-based resistance random access memory.

X-ray spectromicroscopy investigation of soft and hard breakdown in RRAM devices.

Resistive random access memory (RRAM) is considered an attractive candidate for next generation memory devices due to its competitive scalability, low-power operation and high switching speed. The technology however, still faces several challenges that overall prohibit its industrial translation, such as low yields, large switching variability and ultimately hard breakdown due to long-term operation or high-voltage biasing. The latter issue is of particular interest, because it ultimately leads to device failure. In this work, we have investigated the physicochemical changes that occur within RRAM devices as a consequence of soft and hard breakdown by combining full-field transmission x-ray microscopy with soft x-ray spectroscopic analysis performed on lamella samples. The high lateral resolution of this technique (down to 25 nm) allows the investigation of localized nanometric areas underneath permanent damage of the metal top electrode. Results show that devices after hard breakdown present discontinuity in the active layer, Pt inclusions and the formation of crystalline phases such as rutile, which indicates that the temperature increased locally up to 1000 K.