晶体管
电荷(物理)
MOSFET
计算机科学
电气工程
逻辑门
光电子学
电子工程
材料科学
物理
电压
工程类
量子力学
作者
Julien Poupon,Manuel J. Barragán,Andreia Cathelin,Sylvain Bourdel
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:12: 147809-147827
被引量:1
标识
DOI:10.1109/access.2024.3474424
摘要
This paper proposes a design-oriented DC model for MOS transistors in advanced nanometric technologies, based on only six parameters. The proposed model is based on the inversion charge and includes the main short-channel effects for accurately describing the behavior of the transistor DC drain current in all regions (linear to saturation) and regimes of operation (weak to strong inversion). The proposed model is critically compared to existing inversion charge-based models, highlighting the main limitations of previous models presented in the literature and the advantages and disadvantages of our proposal. Then, regarding the model implementation, previously presented inversion charge-based models require a numerical solver to link the transistor’s DC current to its DC node voltages. In this work, we propose an innovative approach to model implementation via the analytical approximation of the Lambert function’s principal branch. Thanks to this approximation, the proposed design-oriented model offers for the first time an analytical single-piece expression of the drain current as an explicit function of the transistor node voltages. The validity of both the proposed transistor DC model and its analytical single-piece implementation is confirmed through simulation and measurement results, using the industrial production-level model UTSOI2 as a reference. The evaluations were conducted on MOS transistors with lengths of 30nm, 60nm, and 150nm in STMicroelectronics 28nm FD-SOI CMOS technology, to validate our results in minimum length, intermediate length, and long transistors in the selected technology. The proposed model achieves an average error of less than 6% in drain current evaluation compared to industry-standard models such as UTSOI2, while significantly reducing computational complexity.
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