Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
| Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 32(2020), 7 vom: 17. Feb., Seite e1905764 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
|
| Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
| Schlagworte: | Journal Article electronic synapses ferroelectric domain switching ferroelectric tunnel junctions neuromorphic computing ultrathin films |
| Zusammenfassung: | © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set |
|---|---|
| Beschreibung: | Date Completed 20.02.2020 Date Revised 30.09.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.201905764 |