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Updated: Aug 7, 2025

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
Published on: May 13, 2020
Highly Reliable Ovonic Threshold Switch with TiN/GeTe/TiN Structure
Dongjun Seong1, Su Yeon Lee1, Hyun Kyu Seo1
1Artificial Intelligence Convergence Research Lab, Sahmyook University, Seoul 01795, Republic of Korea.
A novel ovonic threshold switch (OTS) using TiN/GeTe/TiN effectively suppresses sneak currents in neuromorphic memory systems. This advancement addresses key challenges in next-generation computing architectures.
Area of Science:
- Materials Science
- Computer Engineering
- Electrical Engineering
Background:
- The von Neumann architecture faces power and latency issues, necessitating new computing paradigms.
- Neuromorphic memory systems offer potential for efficient digital information processing.
- Crossbar arrays (CAs) are fundamental to neuromorphic systems but suffer from sneak current issues.
Purpose of the Study:
- To evaluate the electrical characteristics of a TiN/GeTe/TiN ovonic threshold switch (OTS).
- To assess the OTS's suitability as a selector in crossbar arrays for neuromorphic applications.
- To investigate the impact of sneak current on memory cell misoperation and explore OTS solutions.
Main Methods:
- Fabrication of a TiN/GeTe/TiN device structure.
- Characterization of the device's nonlinear DC I-V properties.
- Assessment of device endurance using burst read measurements.
- Evaluation of thermal stability and structural integrity up to 300 °C.
Main Results:
- The TiN/GeTe/TiN OTS demonstrated highly nonlinear DC I-V characteristics.
- The device exhibited excellent endurance exceeding 10^9 cycles.
- A stable threshold voltage below 15 mV/decade was observed.
- Good thermal stability and retention of amorphous structure were confirmed below 300 °C.
Conclusions:
- The TiN/GeTe/TiN OTS is a promising selector for mitigating sneak current in crossbar arrays.
- The device's properties support its application in advanced neuromorphic memory systems.
- This research contributes to overcoming critical obstacles in next-generation computer architectures.
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