Multiobjective Design of 2-D-Material-Based Field-Effect Transistors With Machine Learning Methods
IEEE Transactions on Electron Devices（2021）
摘要
Design optimization of emerging nanoscale transistor technologies often requires careful design tradeoff between many objectives, including speed, power, variability, and so on. By leveraging machine learning (ML) methods, we develop a multiobjective optimization (MOO) framework for 2-D-material-based field-effect transistors (FETs) near the scaling limit. The MOO design framework performs gradien...
更多查看译文
关键词
Field effect transistors,Optimization,Logic gates,Nanoscale devices,Effective mass,Performance evaluation,Capacitance
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要