Ionotronic halide perovskite drift-diffusive synapses for low-power neuromorphic computation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

150 Scopus Citations
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Author(s)

  • Rohit Abraham John
  • Natalia Yantara
  • Yan Fong Ng
  • Govind Narasimman
  • Edoardo Mosconi
  • Daniele Meggiolaro
  • Mohit R. Kulkarni
  • Pradeep Kumar Gopalakrishnan
  • Chien A. Nguyen
  • Filippo De Angelis
  • Subodh G. Mhaisalkar
  • Nripan Mathews

Detail(s)

Original languageEnglish
Article number1805454
Journal / PublicationAdvanced Materials
Volume30
Issue number51
Publication statusPublished - 1 Dec 2018
Externally publishedYes

Abstract

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.

Research Area(s)

  • Halide perovskites, Ion migration, Ionic semiconductors, Neuromorphic computing, Synaptic plasticity

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Ionotronic halide perovskite drift-diffusive synapses for low-power neuromorphic computation. / John, Rohit Abraham; Yantara, Natalia; Ng, Yan Fong et al.
In: Advanced Materials, Vol. 30, No. 51, 1805454, 01.12.2018.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review