神经形态工程学
记忆电阻器
CMOS芯片
加密
计算机科学
国家(计算机科学)
电压
低压
计算机硬件
计算机体系结构
嵌入式系统
电子工程
电气工程
工程类
人工智能
计算机网络
人工神经网络
算法
作者
Bo Sun,Jinhao Zhang,Jieru Song,Jialin Meng,David Wei Zhang,Tianyu Wang,Lin Chen
出处
期刊:InfoMat
[Wiley]
日期:2025-06-30
摘要
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
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