Probing Traps in Ta <sub>2</sub> O <sub>5</sub> /Al <sub>2</sub> O <sub>3</sub> Memristive Switching Devices

Ta2O5/Al2O3 analog memristive devices are promising candidates for next-generation neuromorphic computing applications. This study presents a comprehensive investigation of the charge transport mechanisms in Ta2O5/Al2O3 devices, with a focus on identifying the properties and location of the defects involved in the formation of the conductive filament and analog resistive switching. By integrating experimental measurements with multiphonon charge transport simulations, we show that the relative thickness of the Ta2O5 and Al2O3 layers plays a pivotal role in determining the properties and location of the dominant defects that control the conduction through the RRAM stack. These defects are generated during the formation of the conductive filament and are, therefore, involved in the switching behavior. Sensitivity maps are utilized to pinpoint both the energy levels and spatial positions of defects within the oxide layers that contribute to the switching current. Temperature-dependent current–voltage (I–V) measurements allow us to extract key trap properties, including thermal ionization and relaxation energies, which are used to identify defects in the individual dielectric layers that contribute to the resistive switching. These insights offer valuable guidance for optimizing both the design and performance of these devices for neuromorphic applications.

Authors

Institutions

Publication Details

Journal
ACS Applied Electronic Materials
Published
2025-12-23
DOI
https://doi.org/10.1021/acsaelm.5c01880
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Probing Traps in Ta 2 O 5 /Al 2 O 3 Memristive Switching Devices

Paolo La Torraca, Jisheng Pan, WeiJie Wang, Michel Bosman et al.
ACS Applied Electronic Materials
Advanced Memory and Neural Computing
article

Probing Traps in Ta 2 O 5 /Al 2 O 3 Memristive Switching Devices

Paolo La Torraca, Jisheng Pan, WeiJie Wang, Michel Bosman, Nagarajan Raghavan, Andrea Padovani, Alok Ranjan, Wendong Song, Kin Leong Pey
article en

Abstract

Ta2O5/Al2O3 analog memristive devices are promising candidates for next-generation neuromorphic computing applications. This study presents a comprehensive investigation of the charge transport mechanisms in Ta2O5/Al2O3 devices, with a focus on identifying the properties and location of the defects involved in the formation of the conductive filament and analog resistive switching. By integrating experimental measurements with multiphonon charge transport simulations, we show that the relative thickness of the Ta2O5 and Al2O3 layers plays a pivotal role in determining the properties and location of the dominant defects that control the conduction through the RRAM stack. These defects are generated during the formation of the conductive filament and are, therefore, involved in the switching behavior. Sensitivity maps are utilized to pinpoint both the energy levels and spatial positions of defects within the oxide layers that contribute to the switching current. Temperature-dependent current–voltage (I–V) measurements allow us to extract key trap properties, including thermal ionization and relaxation energies, which are used to identify defects in the individual dielectric layers that contribute to the resistive switching. These insights offer valuable guidance for optimizing both the design and performance of these devices for neuromorphic applications.

ACS Applied Electronic MaterialsVol. 8(1)
Agency for Science, Technology and Research (SG), University of Modena and Reggio Emilia (IT), Singapore University of Technology and Design (SG), National University of Singapore (SG), University College Cork (IE), Japan Science and Technology Agency (JP), Ingegneria dei Trasporti (Italy) (IT), Institute of Materials Research and Engineering (SG), Tyndall Centre (GB), Chalmers University of Technology (SE), Pillar College (US)
Affordable and clean energy
Openalex Percentile: Top 42%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.