Volatile and Nonvolatile Programmable Iontronic Memristor with Lithium Imbued TiO for Neuromorphic Computing Applications.

Rabiul Islam, Yu Shi, Gabriel Vinicius de Oliveira Silva, Manoj Sachdev, Guo-Xing Miao
Author Information
  1. Rabiul Islam: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada. ORCID
  2. Yu Shi: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada.
  3. Gabriel Vinicius de Oliveira Silva: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada. ORCID
  4. Manoj Sachdev: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada.
  5. Guo-Xing Miao: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada. ORCID

Abstract

We demonstrate a lithium (Li) imbued TiO iontronic device that exhibits synapse-like short-term plasticity behavior without requiring a forming process beforehand or a compliance current during switching. A solid-state electrolyte lithium phosphorus oxynitride (LiPON) behaves as the ion source, and the embedding and releasing of Li ions inside the cathodic like TiO renders volatile conductance responses from the device and offers a natural platform for hardware simulating neuron functionalities. Besides, these devices possess high uniformity and great endurance as no conductive filaments are present. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity, were observed with self-relaxation characteristics. Based on the voltage excitation period, the time scale of the volatile memory can be tuned. Temperature measurement reveals the ion displacement-induced conductance channels become frozen below 220 K. In addition, the volatile analog devices can be configured into nonvolatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the nonvolatile units as the weight storage layer. We proceed to use voice recognition as an example with the tunable time constant relationship and obtain 94.4% accuracy with a minimal training data set. Thus, this iontronic platform can effectively process and update temporal information for reservoir and neuromorphic computing paradigms.

Keywords

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Word Cloud

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