title: Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.
abstract: The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al2O3/NbxOy/Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3tunnel barrier and a 2.5 mm thick NbxOymemristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strongI-Vnon-linearity might avoid the need for selector devices in crossbar array structures.
authors.affiliation: Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany.
authors.forename: Mirko
authors.initials: M
authors.lastname: Hansen
authors.affiliation: Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany.
authors.forename: Finn
authors.initials: F
authors.lastname: Zahari
authors.affiliation: Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Ziegler
authors.affiliation: Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany.
authors.forename: Hermann
authors.initials: H
authors.lastname: Kohlstedt
keywords: memristive devices; neural network; neuromorphic systems; spiking neuron; synaptic plasticity; unsupervised learning