材料科学
铁电性
氮化物
电阻随机存取存储器
异质结
光电子学
神经形态工程学
桥接(联网)
电阻式触摸屏
冯·诺依曼建筑
非易失性存储器
纳米技术
电子工程
计算机科学
电气工程
人工神经网络
人工智能
电压
工程类
图层(电子)
计算机网络
电介质
计算机视觉
操作系统
作者
Ding Wang,Ping Wang,Shubham Mondal,Mingtao Hu,Yuanpeng Wu,Tao Ma,Zetian Mi
标识
DOI:10.1002/adma.202210628
摘要
Computing in the analog regime using nonlinear ferroelectric resistive memory arrays can potentially alleviate the energy constraints and complexity/footprint challenges imposed by digital von Neumann systems. Yet the current ferroelectric resistive memories suffer from either low ON/OFF ratios/imprint or limited compatibility with mainstream semiconductors. Here, for the first time, ferroelectric and analog resistive switching in an epitaxial nitride heterojunction comprising ultrathin (≈5 nm) nitride ferroelectrics, i.e., ScAlN, with potentiality to bridge the gap between performance and compatibility is demonstrated. High ON/OFF ratios (up to 105 ), high uniformity, good retention, (<20% variation after > 105 s) and cycling endurance (>104 ) are simultaneously demonstrated in a metal/oxide/nitride ferroelectric junction. It is further demonstrated that the memristor can provide programmability to enable multistate operation and linear analogue computing as well as image processing with high accuracy. Neural network simulations based on the weight update characteristics of the nitride memory yielded an image recognition accuracy of 92.9% (baseline 96.2%) on the images from Modified National Institute of Standards and Technology. The non-volatile multi-level programmability and analog computing capability provide first-hand and landmark evidence for constructing advanced memory/computing architectures based on emerging nitride ferroelectrics, and promote homo and hybrid integrated functional edge devices beyond silicon.
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