A Reading Method for 2T2R RRAM Arrays Coding their Multiple States

Resistive random-access memory (RRAM) devices have been frequently mentioned considering their robust performances. The conventional transistor one-memristor (1TIR) RRAM device becomes less competitive considering its multi- level operation. This paper introduces a two-transistor two- memristor (2T2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex>) structure as well as a reading method for the configuration. The designed sense circuit based on a Strong-Arm latch codes 9 different states with 6 bits in 48ns and consumes a power of 7.6uW and it also demonstrates a tolerance to RRAM technologies and variations.

Authors

Institutions

Publication Details

Published
2024-08-11
DOI
https://doi.org/10.1109/mwscas60917.2024.10658733
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

A Reading Method for 2T2R RRAM Arrays Coding their Multiple States

Amelie Hagelauer, Running Guo, Stefan Pechmann
Advanced Memory and Neural Computing
article

A Reading Method for 2T2R RRAM Arrays Coding their Multiple States

Amelie Hagelauer, Running Guo, Stefan Pechmann
article en

Abstract

Resistive random-access memory (RRAM) devices have been frequently mentioned considering their robust performances. The conventional transistor one-memristor (1TIR) RRAM device becomes less competitive considering its multi- level operation. This paper introduces a two-transistor two- memristor (2T2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex>) structure as well as a reading method for the configuration. The designed sense circuit based on a Strong-Arm latch codes 9 different states with 6 bits in 48ns and consumes a power of 7.6uW and it also demonstrates a tolerance to RRAM technologies and variations.

Technical University of Munich (DE)
Quality Education
Openalex Percentile: Top 87%
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.