Design-Oriented Single-Piece Explicit I-V DC Charge-Based Model for MOS Transistors in Nanometric Technologies
Open Access
- 4 October 2024
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 12, 147809-147827
Abstract
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 productionlevel model UTSOI2 as a reference. The evaluations were conducted on MOS transistors with lengths of 30 nm, 60 nm, and 150 nm in STMicroelectronics 28 nm 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.Keywords
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