<scp>CMOS</scp> compatible multi‐state memristor for neuromorphic hardware encryption with low operation voltage

Abstract Different from traditional software encryption, hardware encryption shows obvious advantages in AI information encryption application scenarios with high reliability and high security requirements. With the development of memristors, memristor‐based hardware encryption attracted the interests of researchers in secure communication. Hafnium‐based memristors have received widespread attention due to fast speed, low power consumption, and compatibility with CMOS technology. In this study, a HfAlO x ‐based memristor with an ON/OFF ratio of &gt;10 4 , an endurance characteristic of 10 5 cycles, and a low operating voltage of 0.56 V/−0.135 V was proposed. Eight‐level states were achieved and used to design a hardware encryption scheme through a neural network. Parallel information encryption operations of “S” “D” “U” were realized in a memristor array. By constructing an artificial neural network, the recognition rate of encrypted letters without/with memristor is 62.3% and 98.1%, respectively. The memristor‐based encryption scheme further expands the choices and application prospects of hardware encryption. image

Authors

Institutions

Publication Details

Journal
InfoMat
Published
2025-06-30
DOI
https://doi.org/10.1002/inf2.70044
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.65

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CMOS compatible multi‐state memristor for neuromorphic hardware encryption with low operation voltage

Jialin Meng, David Wei Zhang, Bo Sun, Tianyu Wang et al.
5 citations
InfoMat
Advanced Memory and Neural Computing
3.65
article

CMOS compatible multi‐state memristor for neuromorphic hardware encryption with low operation voltage

Jialin Meng, David Wei Zhang, Bo Sun, Tianyu Wang, Lin Chen, Jieru Song, Jinhao Zhang
article en
5 citations

Abstract

Abstract Different from traditional software encryption, hardware encryption shows obvious advantages in AI information encryption application scenarios with high reliability and high security requirements. With the development of memristors, memristor‐based hardware encryption attracted the interests of researchers in secure communication. Hafnium‐based memristors have received widespread attention due to fast speed, low power consumption, and compatibility with CMOS technology. In this study, a HfAlO x ‐based memristor with an ON/OFF ratio of &gt;10 4 , an endurance characteristic of 10 5 cycles, and a low operating voltage of 0.56 V/−0.135 V was proposed. Eight‐level states were achieved and used to design a hardware encryption scheme through a neural network. Parallel information encryption operations of “S” “D” “U” were realized in a memristor array. By constructing an artificial neural network, the recognition rate of encrypted letters without/with memristor is 62.3% and 98.1%, respectively. The memristor‐based encryption scheme further expands the choices and application prospects of hardware encryption. image

InfoMatVol. 7(11)
Shandong University (CN), Chinese Academy of Sciences (CN), Fudan University (CN), Suzhou Research Institute (CN), Shanghai Fudan Microelectronics (China) (CN), Shanghai Institute of Microsystem and Information Technology (CN)
National Natural Science Foundation of China, Fudan University, Natural Science Foundation of Shandong Province, Taishan Scholar Foundation of Shandong Province, National Key Research and Development Program of China
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.65
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.