Reconfigurable artificial neuron and synapse enabled through a single alloyed memristor - Elias Passerini; Mila Lewerenz; Arnaud Schneuwly; Nadia Jimenez Olalla; Markus Fischer; Raphael Gisler; Luiz Felipe Aguinsky; Alexandros Emboras; Yuriy Fedoryshyn; Mathieu Luisier; Thomas Schimmel; Miklós Csontos; et al. (Scientific Reports)
Abstract Memristive devices have drawn significant interest due to their use in novel paradigms such as neuromorphic computing. Neuromorphic systems are developed by implementing artificial neurons and synapses on a hardware level. Hence, memristors with multipurpose and reconfigurable neuromorphic functionalities could be highly beneficial in the design process. In this study, we experimentally verify that both neuronal and synaptic functions can be implemented on a single memristor. By controlling the device current at two different levels, the memristor operates in either a volatile or a nonvolatile retention regime. These two operation regimes are essential to mimic neuronal or synaptic behavior. Towards this end, we use an alloyed filamentary memristor (AgSn/SiO2/Pt) composed of ions with differing mobilities enabling both integrate and fire (IF) operation in the volatile regime and synaptic weights in the nonvolatile regime. By only changing the current compliance, these devices switch reliably between the aforementioned retention regimes. Additionally, our proposed training method significantly improves switching variability in the volatile regime. We show how the mean set voltage statistically reduce from 1.2 to 0.2 V; and the standard deviation of the set voltages reduced from 0.52 to 0.03 V.