Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation

Bibliographische Detailangaben
Veröffentlicht in:	Advanced materials (Deerfield Beach, Fla.). - 1998. - 30(2018), 51 vom: 30. Dez., Seite e1805454
1. Verfasser:	John, Rohit Abraham (VerfasserIn)
Weitere Verfasser:	Yantara, Natalia, Ng, Yan Fong, Narasimman, Govind, Mosconi, Edoardo, Meggiolaro, Daniele, Kulkarni, Mohit R, Gopalakrishnan, Pradeep Kumar, Nguyen, Chien A, De Angelis, Filippo, Mhaisalkar, Subodh G, Basu, Arindam, Mathews, Nripan
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2018
Zugriff auf das übergeordnete Werk:	Advanced materials (Deerfield Beach, Fla.)
Schlagworte:	Journal Article halide perovskites ion migration ionic semiconductors neuromorphic computing synaptic plasticity

Beschreibung
Zusammenfassung:	© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short- and long-term plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material
Beschreibung:	Date Completed 17.12.2018 Date Revised 30.09.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE
ISSN:	1521-4095
DOI:	10.1002/adma.201805454