电容器
感知器
材料科学
人工神经网络
计算机科学
电极
模式识别(心理学)
电介质
人工智能
光电子学
分析化学(期刊)
电气工程
物理
化学
电压
工程类
色谱法
量子力学
作者
J. Muñoz-Gorriz,Scott Monaghan,K. Cherkaoui,J. Suñé,Paul K. Hurley,E. Miranda
标识
DOI:10.1109/ipfa53173.2021.9617281
摘要
In this work, we investigated the spatial distribution of failure sites in large area Pt/HfO 2 /Pt capacitors using simple neural networks as classifiers. When an oxide breakdown (BD) occurs due to severe electrical stress, a mark shows up in the top metal electrode at the location where the failure event took place. The mark is the result of a microexplosion occurring inside the dielectric film. Large area devices need to be studied because the number of generated spots must be the required for statistical analysis. The obtained results using multilayer perceptrons with different number of neurons and hidden layers indicate that the largest breakdown spots tend to concentrate towards the center of the device. This observation is consistent with previous exploratory analysis carried out using spatial statistics techniques. This exercise shows the suitability of multilayer perceptrons for investigating the distribution of failure sites or defects on a given surface.
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