TY  - JOUR
AU  - Bengel, Christopher
AU  - Cüppers, Felix
AU  - Payvand, Melika
AU  - Dittmann, Regina
AU  - Waser, R.
AU  - Hoffmann-Eifert, Susanne
AU  - Menzel, Stephan
TI  - Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns
JO  - Frontiers in neuroscience
VL  - 15
SN  - 1662-453X
CY  - Lausanne
PB  - Frontiers Research Foundation
M1  - FZJ-2021-02533
SP  - 661856
PY  - 2021
AB  - With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called “edge computing.” Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO2/TiOx nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.
LB  - PUB:(DE-HGF)16
C6  - 34163323
UR  - <Go to ISI:>//WOS:000663741500001
DO  - DOI:10.3389/fnins.2021.661856
UR  - https://juser.fz-juelich.de/record/893059
ER  -