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Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching.
Yiyang Li1, Elliot J Fuller1, Joshua D Sugar1
1Sandia National Laboratories, Livermore, CA, 94550, USA.
Advanced Materials (Deerfield Beach, Fla.)
|September 23, 2020
Summary
Researchers developed filament-free bulk resistive random access memory (RRAM) using yttria-stabilized zirconia. This overcomes stochastic switching in traditional RRAM, enabling predictable analogue states for energy-efficient neuromorphic computing.
Area of Science:
- Materials Science
- Nanoelectronics
- Computer Engineering
Background:
- Digital computing faces physical limits due to increasing energy demands.
- Analogue-memory-based neuromorphic computing offers energy efficiency for tasks like deep neural networks.
- Current analogue resistive memory (RRAM) is limited by unpredictable switching in filamentary structures.
Purpose of the Study:
- To overcome the stochastic switching limitations in filamentary RRAM.
- To develop a predictable analogue memory for energy-efficient neuromorphic computing.
- To demonstrate a novel approach for engineering deterministic nanoelectronic materials.
Main Methods:
- Incorporation of a solid electrolyte interlayer (yttria-stabilized zirconia) to eliminate filaments in RRAM.
- Development of filament-free, bulk-RRAM cells storing analogue states via bulk point defect concentration.
- Utilizing titanium dioxide (TiO2-x) switching layers and YSZ electrolytes.
Main Results:
- Demonstrated deterministic and linear analogue switching in bulk-RRAM devices.
- Showcased predictable switching by leveraging the ensemble behavior of oxygen vacancy defects.
- Experiments and modeling confirmed the effectiveness of the YSZ interlayer and TiO2-x switching layers.
Conclusions:
- Bulk-RRAM with a YSZ interlayer solves memristor unpredictability issues, paving the way for commercialization.
- This technology enables new applications for energy-efficient neuromorphic computing.
- Harnessing bulk point defects in ionic materials offers a route to deterministic nanoelectronic devices beyond RRAM.
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