CMOS Compatible Hf0.5Zr0.5O2 Ferroelectric Tunnel Junctions for Neuromorphic Devices. (5th August 2019)

• Record Type: Journal Article
• Authors: Mittermeier, Bernhard; Dörfler, Andreas; Horoschenkoff, Anna; Katoch, Rajesh; Schindler, Christina; Ruediger, Andreas; Kolhatkar, Gitanjali
• Is Part Of: Advanced intelligent systems. Volume 1:Number 5(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-05
• Subjects: ferroelectric tunnel junctions -- Hf0.5Zr0.5O2 -- nucleation-limited-switching models -- spike-timing-dependent plasticity -- synaptic learning rate; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.201900034
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : While machine learning algorithms are becoming more and more elaborate, their underlying artificial neural networks most often still rely on the binary von Neumann computer architecture. However, artificial neural networks access their full potential when combined with gradually switchable artificial synapses. Herein, complementary metal oxide semiconductor‐compatible Hf0.5 Zr0.5 O2 ferroelectric tunnel junctions fabricated by radio‐frequency magnetron sputtering are used as artificial synapses. On a single synapse level, their neuromorphic behavior is quantitatively investigated with spike‐timing‐dependent plasticity. It is found that the learning rate of the synapses mainly depends on the voltage amplitude of the applied stimulus. The experimental findings are well reproduced with simulations based on the nucleation‐limited‐switching model. Abstract : The spike‐timing‐dependent plasticity of Hf0.5 Zr0.5 O2 ferroelectric tunnel junctions is investigated quantitatively for bio‐inspired neuromorphic computing. The learning rate is controlled by modifying the maximum amplitude of the action potential due to a change in the amount of switched ferroelectric domains. The experimental findings are reproduced with the nucleation‐limited‐switching model.