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Related Experiment Video

Updated: Nov 5, 2025

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

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Published on: May 13, 2020

Self-rectifying resistive memory in passive crossbar arrays.

Kanghyeok Jeon^1,2, Jeeson Kim², Jin Joo Ryu¹

¹Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu, Daejeon, Republic of Korea.

Nature Communications

|May 21, 2021

Summary

This summary is machine-generated.

A new self-rectifying resistive memory cell (SRMC) offers improved performance for deep learning acceleration. This memory-centric computing solution enhances energy efficiency and reliability for advanced AI applications.

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Area of Science:

Materials Science
Computer Engineering
Electrical Engineering

Background:

Conventional computing architectures struggle with deep learning workloads.
Memory-centric computing is emerging as a solution to accelerate AI.
Resistive memory cells (SRMCs) are key components for non-volatile memory.

Purpose of the Study:

To introduce a novel trilayer SRMC for memory-centric computing.
To demonstrate the SRMC's suitability for deep learning acceleration.
To evaluate the SRMC's performance metrics and scalability.

Main Methods:

Fabrication of a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2) SRMC.
Characterization of SRMC electrical properties: selectivity, read power, latency, non-volatility, and endurance.
Testing of SRMCs in passive crossbar arrays of varying sizes (30x30 to 320x320).

Main Results:

The SRMC exhibits high selectivity (10^4), two-bit operation, and low read power (0.8-4 nW).
Achieved read latency (<10 μs) and excellent non-volatility (>10^4 s retention at 85°C).
Demonstrated feasible operation in large-scale passive crossbar arrays, confirming scalability.

Conclusions:

The developed SRMC significantly outperforms previous SRMCs for memory-centric computing.
Its characteristics are ideal for improving deep learning acceleration and energy efficiency.
The SRMC shows strong potential for large-scale, high-density non-volatile memory applications.