Memristive tonotopic mapping with volatile resistive switching memory devices

Abstract

To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems.

Importance of growth method and substrate-induced crystalline quality in Al/Gd0.2Ca0.8MnO3/Au memristor devices

“…Memristor devices are comprised of a simple structure with a material with a switchable resistance through a process called resistive switching (RS), which works as a storage medium, between two metal electrodes [13]. The switching layer can be composed of simple oxides such as AlO x [14], HfO 2 [15], TaO 2 [16], or more complex oxides like the so-called perovskites structure materials (e.g. manganites, titanates, cobaltites, etc) [17,18], which are particularly intriguing due to their intrinsic forming-free behavior and quasi-continuous resistive modulation.…”