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Published on: April 12, 2018
Tunable Switching Mechanisms in HfZrO2-Based Tunnel Junctions for High-Performance Synaptic Arrays.
Jiwon You1, Jeong-Han Kim2, Minsuk Song3
1Department of AI Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea.
Optimized ferroelectric tunnel junction (FTJ) arrays with hybrid switching exhibit high tunneling electroresistance (TER) for efficient neuromorphic computing. These scalable arrays enable advanced nonvolatile memory and energy-efficient AI hardware.
Area of Science:
- Materials Science
- Solid State Physics
- Device Engineering
Background:
- Ferroelectric tunnel junctions (FTJs) are key components for next-generation nonvolatile memory and neuromorphic computing.
- Scalable integration of FTJs into large arrays is essential for practical applications.
- Controlling switching mechanisms in FTJs is crucial for optimizing device performance.
Purpose of the Study:
- To investigate materials and structural engineering for scalable hybrid-switching FTJ arrays.
- To systematically manipulate oxygen vacancy concentrations in HfZrO2 (HZO) films.
- To achieve distinct operational modes and enhance tunneling electroresistance (TER) performance.
Main Methods:
- Strategic selection of bottom electrodes (Mo, Mo/Ti) and interfacial layers (ZrO2).
- Controlled manipulation of oxygen vacancy (VO) concentrations in HfZrO2 films.
- Fabrication of a 42x42 FTJ array and integration into a Vision Transformer (ViT) architecture.
Main Results:
- Achieved three operational modes: pure ferroelectric, defect-modulated, and hybrid switching.
- Demonstrated high TER ratios: ~10^2 with Mo, >10^4 with Mo/Ti bottom electrodes.
- Fabricated FTJ arrays showed uniform multi-level conductance modulation and stable VMM operations in ViT.
Conclusions:
- Precisely engineered hybrid-switching FTJ arrays offer a scalable and energy-efficient platform for advanced memory and neuromorphic systems.
- Material and structural optimization is critical for achieving high-performance FTJ devices.
- Integrated FTJ arrays enable stable and efficient AI computations, overcoming device variability challenges.
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