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使用人工神经网络对先进的Gate-All-Around纳米板FET进行紧建模

Yage Zhao¹, Zhongshan Xu¹, Huawei Tang¹

¹School of Microelectronics, Fudan University, Shanghai 200433, China.

|February 24, 2024

概括

人工神经网络 (ANN) 能够为先进的通道全方位 (GAA) 纳米板 FET 提供准确的紧建模. 这种方法精确地预测了晶体管特性,这对于不断发展的逻辑设备架构至关重要.

科学领域:

半导体设备物理学半导体设备物理
计算电子学计算电子学
材料科学是一种材料科学.

背景情况:

在逻辑设备中,向通道全方位 (GAA) 架构的演变需要先进的晶体管建模.
对于超级晶体管来说,准确和快速的紧建模是必不可少的,特别是考虑到尺寸变化.

研究的目的:

开发基于人工神经网络 (ANN) 的方法论,用于GAA纳米板FET (NSFET) 的紧模型生成.
评估ANN模型在捕获这些先进晶体管的直流和交流特性的精度.

主要方法:

利用 TCAD 模拟来获得 GAA NSFET 在各种物理维度 (Lg, Wsh, Tsh) 和工作电压 (Vgs, Vds) 的直流和交流特征.
开发和评估了一个高精度的ANN模型架构.
系统地研究了ANN参数 (大小,激活功能,学习速度,时代) 对模型准确性的影响.

主要成果:

提出了一个浅层神经网络配置,以实现最佳的ANN模型生成.
证明了优化的ANN模型准确地重现了DC和AC特征.
取得了0.01.的非常低的适配误差 (平均平方误差 - MSE).

结论:

ANNs为GAA NSFETs的紧建模提供了一个高度准确和高效的方法.
开发的方法有效地捕捉了各种条件和维度变化的设备行为.
这项工作支持超级晶体管设计和模拟的进步.

关键词:

这是一个TCAD模拟.人工神经网络 (ANN) 是一个人工神经网络.紧型的模型是紧型的模型.全面通道 (GAA) 纳米板FETs (NSFETs) 的使用

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