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Updated: Jul 16, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing.

Sarfraz Ali¹, Muhammad Abaid Ullah², Ali Raza³

¹Department of Physics, Riphah International University, Lahore Campus, 13-KM Raiwand Road, Lahore 54000, Pakistan.

Nanomaterials (Basel, Switzerland)

|September 9, 2023

Summary

Cerium oxide (CeO2) shows promise for resistive random-access memories (RRAMs) due to its stability and potential in neuromorphic computing. This review details CeO2-based memristors for advanced synaptic applications.

Keywords:

CeO2 RRAM memristive devices capping layers filamentary mechanisms neuromorphic learning systems switching layers symmetric and asymmetric electrodes synaptic devices

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Published on: May 13, 2020

Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Cerium oxide (CeO2) exhibits unique properties like multiple oxidation states and excellent resistive-switching (RS) characteristics.
These properties make CeO2 a strong candidate for resistive random-access memories (RRAMs) and neuromorphic computing applications.

Purpose of the Study:

To comprehensively review recent advancements in CeO2-based RRAMs.
To explore the filamentary mechanisms, device structures, and neuromorphic applications of CeO2 memristors.

Main Methods:

Review of existing literature on CeO2-based memristors.
Analysis of unipolar, bipolar, and threshold memristive behaviors.
Examination of electrode engineering and device structures (single-layer, bilayer, doped).

Main Results:

CeO2 demonstrates remarkable RS uniformity, cycling endurance, and data retention.
Various CeO2-based memristor structures show potential as synaptic devices.
Neuromorphic applications, including spike-based learning and plasticity, are extensively studied.

Conclusions:

CeO2-based memristors are highly promising for advanced synaptic studies and neuromorphic computing.
Further research utilizing high-dielectric-constant oxide memristors is encouraged.