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Updated: Apr 1, 2026

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In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
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All-Oxide ITO/HZO/WOx Ferroelectric Tunnel Junctions with Oxygen-Engineered Interfaces for Highly Endurable
Seungjoon Jeong1, Huiseong Shin1, Myeongjae Choi1
1School of Electrical Engineering, Korea University, Seoul 02841, South Korea.
ACS Applied Materials & Interfaces
|March 31, 2026
Summary
This study presents a simple ferroelectric tunnel junction (FTJ) synapse for neuromorphic computing. The engineered interface and electrodes enable stable switching and multilevel conductance, crucial for efficient AI hardware.
Area of Science:
- Materials Science
- Solid State Physics
- Computer Engineering
Background:
- Ferroelectric tunnel junctions (FTJs) are key for neuromorphic computing due to their compact structure and multilevel conductance.
- Existing FTJ synapses often require complex multilayer stacks, hindering scalability and device integration.
- Developing simpler FTJ structures with stable interfacial transport is essential for advancing neuromorphic hardware.
Purpose of the Study:
- To demonstrate a structurally simple metal-ferroelectric-metal (MFM) FTJ synapse.
- To engineer the interface and electrode properties for stable switching without additional layers.
- To achieve reliable analog weight modulation for neuromorphic applications.
Main Methods:
- Fabrication of an ITO/Hf0.5Zr0.5O2/WOx stack.
- Tuning the oxygen stoichiometry of the WOx bottom electrode to create a trap-rich interface.
- Utilizing an ITO top electrode to optimize electric field distribution.
- Characterization of device performance, including resistance ratio, endurance, and conductance states.
Main Results:
- Demonstrated a structurally simple MFM FTJ (ITO/Hf0.5Zr0.5O2/WOx) without additional insertion layers.
- Achieved stable switching via polarization-modulated trap-assisted tunneling by tuning WOx stoichiometry.
- The optimized FTJ showed a resistance ratio of ~100, endurance >10^8 cycles, and 64 conductance states.
- Exhibited stable spike-dependent plasticity and analog weight modulation suitable for neuromorphic operation.
Conclusions:
- Simple MFM FTJ synapses with engineered interfaces offer a scalable solution for neuromorphic and in-memory computing.
- The demonstrated device achieves high performance metrics and reliable analog behavior.
- Neural network simulations using experimental data achieved 91.5% accuracy on the MNIST dataset, validating the device's potential.
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