记忆电阻器
材料科学
神经形态工程学
纳米技术
电阻随机存取存储器
横杆开关
计算机数据存储
光电子学
电极
纳米晶
电压
计算机科学
电子工程
电气工程
人工神经网络
计算机硬件
工程类
电信
化学
物理化学
机器学习
作者
Mubashir Mushtaq Ganaie,Amit Kumar,Amit Kumar Shringi,Satyajit Sahu,Michael A. Saliba,Mahesh Kumar
标识
DOI:10.1002/adfm.202405080
摘要
Abstract In conventional designs, sensory systems are segregated from memory and computing units. The conversion and transmission of data from analog sensing domains to digital storage result in inefficient power utilization and increased latency. Here, a multifunctional memristor capable of detecting gamma radiation while also serving as a data storage device and an artificial synapse is reported. Large‐scale integration of oxide‐based memristors for artificial neural networks faces major challenges due to the sneak‐path current issue in crossbar arrays. Consequently, material combinations and fabrication variables significantly shape nanoscale processes, which are essential in determining resistive switching properties and functionalities. Resistive switching in AlFeO 3 is studied using different electrode materials (silver (Ag), gold (Au), chromium (Cr), fluorine‐doped tin oxide, and silicon), embedding metal (Ag, Au) nanocrystals to engineer a class of tunable memories capable of functioning as selector, memory, artificial synapse, and dosimeter. Techniques like electrode engineering, nanocrystal seeding, and temperature‐dependent thin film deposition are employed to tune resistive and threshold switching functionalities. Accessing different functionalities requires changing the electrode materials or changing the synthesis conditions of the AlFeO 3 resistive switching layer and are not interconvertible in the same device simultaneously. The devices emulate critical neural functions and demonstrate interconversion dynamics between short‐term and long‐term plasticity.
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