Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications

Author:

Byun Jisu1,Kho Wonwoo1,Hwang Hyunjoo1,Kang Yoomi1,Kang Minjeong1,Noh Taewan1,Kim Hoseong1,Lee Jimin1,Kim Hyo-Bae2,Ahn Ji-Hoon2ORCID,Ahn Seung-Eon13ORCID

Affiliation:

1. Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea

2. Department of Materials Science and Chemical Engineering, Hanyang University, Ansan 15588, Republic of Korea

3. Department of Nano & Semiconductor Eng, Tech University of Korea, Siheung 05073, Republic of Korea

Abstract

The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals.

Funder

Ministry of Science and ICT

Korean Government

Publisher

MDPI AG

Subject

General Materials Science,General Chemical Engineering

Reference34 articles.

1. Emerging materials for neuromorphic devices and systems;Kim;Iscience,2020

2. Spike-timing dependent plasticity;Gerstner;Spike-Timing Depend. Plast.,2010

3. Self-organized computation with unreliable, memristive nanodevices;Snider;Nanotechnology,2007

4. RRAM-based synapse devices for neuromorphic systems;Moon;Faraday Discuss.,2019

5. Oxide-based RRAM materials for neuromorphic computing;Hong;J. Mater. Sci.,2018

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