论文标题
用于模拟神经网络在线培训的铁电FET的计算有效的紧凑模型
A computationally efficient compact model for ferroelectric FETs for the simulation of online training of neural networks
论文作者
论文摘要
具有HF0.5ZR0.5O2的三栅极铁电FET用于内存和神经形态应用，并为多级操作制造并表征了神经形态应用。电导和阈值电压具有高度线性和对称特性。开发了一个紧凑的分析模型，以准确捕获FET转移特性，包括串联电阻，库仑散射和垂直场依赖性迁移率降解效应，以及阈值电压和迁移率以及铁电偏振开关的发展。该模型涵盖了子阈值和强大的反转操作。与基于载波的内存操作相比，其他测量确认了铁电开关。紧凑型模型是在模拟平台中实现的，用于在线培训深神经网络。
Tri-gate ferroelectric FETs with Hf0.5Zr0.5O2 gate insulator for memory and neuromorphic applications are fabricated and characterized for multi-level operation. The conductance and threshold voltage exhibit highly linear and symmetric characteristics. A compact analytical model is developed to accurately capture FET transfer characteristics, including series resistance, coulombic scattering, and vertical field dependent mobility degradation effects, as well as the evolvement of threshold voltage and mobility with ferroelectric polarization switching. The model covers both sub-threshold and strong inversion operation. Additional measurements confirm ferroelectric switching as opposed to carrier-trapping-based memory operation. The compact model is implemented in a simulation platform for online training of deep neural networks.
