论文标题
FinFET及以后的紧凑型设备模型
Compact Device Models for FinFET and Beyond
论文作者
论文摘要
紧凑的设备型号在连接设备技术和电路设计中起着重要作用。 BSIM-CMG和BSIM-IMG分别是适合FinFET和UTBB技术的行业标准紧凑型模型。它的基于表面潜在的建模框架和对称性保存属性使其适合模拟/RF和数字设计。在人工智能 /深度学习时代，紧凑的模型进一步增强了我们探索RRAM和其他基于NVM的神经形态电路的能力。我们已经证明了NCKU的基于Verilog-A的紧凑型模型对RRAM神经形态电路进行了模拟。进行大型机器学习模拟的进一步抽象以实现大规模的机器学习模拟。
Compact device models play a significant role in connecting device technology and circuit design. BSIM-CMG and BSIM-IMG are industry standard compact models suited for the FinFET and UTBB technologies, respectively. Its surface potential based modeling framework and symmetry preserving properties make them suitable for both analog/RF and digital design. In the era of artificial intelligence / deep learning, compact models further enhanced our ability to explore RRAM and other NVM-based neuromorphic circuits. We have demonstrated simulation of RRAM neuromorphic circuits with Verilog-A based compact model at NCKU. Further abstraction with macromodels is performed to enable larger scale machine learning simulation.
