Tri-Gate Ferroelectric FET Characterization and Modelling for Online Training of Neural Networks at Room Temperature and 233K

S. De, M. Baig, Bo-Han Qiu, D. Lu, P. Sung, F. Hsueh, Yao-Jen Lee, C. Su
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引用次数: 12

Abstract

This paper reports detailed analysis on switching dynamics and device variability over a wide range of temperatures for deeply scaled (40nm gate length) tri-gate ferroelectric FETs with 10nm HZO fabricated using gate first process on SOI wafers. Our experimental results manifest, 99% ferroelectric switching at room temperature and at 233K. A memory window over 5V and strong gate length dependence of memory window is observed. Highly linear and symmetric multilevel switching characteristics makes our ferroelectric FETs suitable for neuromorphic applications, as demonstrated with neural network online training simulations.
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室温和233K下神经网络在线训练的三栅极铁电场效应晶体管表征和建模
本文详细分析了深度缩放(40nm栅极长度)三栅极铁电场效应管在宽温度范围内的开关动力学和器件可变性,该三栅极铁电场效应管采用栅极优先工艺在SOI晶圆上制造10nm HZO。实验结果表明,在室温和233K下,铁电开关率达到99%。观察到5V以上的记忆窗和强门长依赖性的记忆窗。高度线性和对称的多电平开关特性使我们的铁电场效应管适用于神经形态应用,正如神经网络在线训练模拟所证明的那样。
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