ArticleMaterials (Basel, Switzerland)2024
Implementation of Artificial Synapse Using IGZO-Based Resistive Switching Device.
Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Recent Advances and Perspectives on Field-Effect Transistors for Artificial Visual Neuromorphic Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Layer-by-Layer Nanoarchitectonics: A Method for Everything in Layered Structures.Materials (Basel, Switzerland) · 2025Review
- Materials Nanoarchitectonics for Advanced Devices.Materials (Basel, Switzerland) · 2024Review
- Oxygen-Plasma-Treated Al/TaOBiomimetics (Basel, Switzerland) · 2024Article
- Liquid-Liquid and Liquid-Solid Interfacial Nanoarchitectonics.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this study, we present the resistive switching characteristics and the emulation of a biological synapse using the ITO/IGZO/TaN device. The device demonstrates efficient energy consumption, featuring low current resistive switching with minimal set and reset voltages. Furthermore, we establish that the device exhibits typical bipolar resistive switching with the coexistence of non-volatile and volatile memory properties by controlling the compliance during resistive switching phenomena. Utilizing the IGZO-based RRAM device with an appropriate pulse scheme, we emulate a biological synapse based on its electrical properties. Our assessments include potentiation and depression, a pattern recognition system based on neural networks, paired-pulse facilitation, excitatory post-synaptic current, and spike-amplitude dependent plasticity. These assessments confirm the device's effective emulation of a biological synapse, incorporating both volatile and non-volatile functions. Furthermore, through spike-rate dependent plasticity and spike-timing dependent plasticity of the Hebbian learning rules, high-order synapse imitation was done.
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Registered trials
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