Statistically modeling I-V characteristics of CNT-FET with LASSO

作     者：Dongsheng Ma Zuochang Ye Yan Wang 

作者机构：Institute of MicroelectronicsTsinghua University 

出 版 物：《Journal of Semiconductors》 (半导体学报（英文版）)

年 卷 期：2017年第38卷第8期

页      面：28-31页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

主　　题：statistical learning compact model CNT-FET I-V characteristics LASSO machine learning 

摘      要：With the advent ofinternet of things （lOT）, the need for studying new material and devices for various applications is increasing. Traditionally we build compact models for transistors on the basis of physics. But physi- cal models are expensive and need a very long time to adjust for non-ideal effects. As the vision for the application of many novel devices is not certain or the manufacture process is not mature, deriving generalized accurate physi- cal models for such devices is very strenuous, whereas statistical modeling is becoming a potential method because of its data oriented property and fast implementation. In this paper, one classical statistical regression method, LASSO, is used to model the I-V characteristics of CNT-FET and a pseudo-PMOS inverter simulation based on the trained model is implemented in Cadence. The normalized relative mean square prediction error of the trained model versus experiment sample data and the simulation results show that the model is acceptable for digital circuit static simulation. And such modeling methodology can extend to general devices.