<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 >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
- Jialin Meng (ORCID: https://orcid.org/0000-0002-4622-833X)
- David Wei Zhang
- Bo Sun (ORCID: https://orcid.org/0000-0001-9346-6157)
- Tianyu Wang (ORCID: https://orcid.org/0000-0001-8114-3481)
- Lin Chen (ORCID: https://orcid.org/0000-0002-7145-7564)
- Jieru Song (ORCID: https://orcid.org/0009-0007-1034-6645)
- Jinhao Zhang (ORCID: https://orcid.org/0009-0001-3898-4901)
Institutions
- 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)
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
- 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