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Coexisting Volatile and Nonvolatile Switching in 3D ALD-IGZO Vertical RRAM for Fully Hardware-Based Wide Reservoir
Seeun Lee1, Jaewon Park2, Nawoon Kim1
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 11, 2026
Summary
This study introduces a novel indium-gallium-zinc oxide (IGZO) vertical resistive random-access memory (VRRAM) platform for neuromorphic computing. The device enables efficient hardware-based reservoir computing, achieving high accuracy on the MNIST dataset.
Area of Science:
- Materials Science
- Neuromorphic Engineering
- Semiconductor Device Physics
Background:
- Indium-gallium-zinc oxide (IGZO) is prevalent in thin-film transistors (TFTs).
- IGZO's potential in vertical resistive random-access memory (VRRAM) remains underexplored.
- VRRAM architectures offer advantages in device scaling and integration.
Purpose of the Study:
- To develop and characterize an ALD-IGZO VRRAM platform for novel applications.
- To demonstrate a dual-mode switching behavior within the IGZO VRRAM device.
- To implement a hardware-based reservoir computing system using the developed VRRAM.
Main Methods:
- Atomic Layer Deposition (ALD) of IGZO for conformal coating on vertical sidewalls.
- Fabrication of a dual-mode VRRAM device exhibiting volatile and nonvolatile switching.
- Integration of multiple VRRAM sub-reservoirs for parallel operation in a wide reservoir computing architecture.
Main Results:
- Achieved uniform and reliable switching with excellent step coverage and compositional control.
- Demonstrated dual-mode operation: volatile (reservoir layer) and nonvolatile (readout layer).
- Implemented a wide reservoir computing system achieving 91.3% accuracy on the MNIST dataset with low energy consumption (3.70 nJ/classification).
Conclusions:
- The ALD-IGZO VRRAM platform offers a scalable route for in-memory neuromorphic computing.
- The dual-mode switching capability is crucial for advanced computing paradigms like reservoir computing.
- This work paves the way for energy-efficient, learning-capable neuromorphic systems.
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