RT Journal Article
T1 Pelgrom-based predictive model to estimate metal grain granularity and line edge roughness in advanced multigate MOSFETs
A1 García Fernández, Julián
A1 Seoane Iglesias, Natalia
A1 Comesaña Figueroa, Enrique
A1 García Loureiro, Antonio Jesús
K1 Field effect transistors
K1 Logic gates
K1 FinFETs
K1 Threshold voltage
K1 Predictive models
K1 Electron devices
K1 Computer architecture
AB The impact of different variability sources on the transistor performance increases as devices are scaled-down, being the metal grain granularity (MGG) and the line edge roughness (LER) some of the major contributors to this increase. Variability studies require the simulation of large samples of different device configurations to have statistical significance, increasing the computational cost. A novel Pelgrom-based predictive (PBP) model that estimates the impact of MGG and LER through the study of the threshold voltage standard deviation (σ VT h), is proposed. This technique is computationally efficient since once the threshold voltage mismatch is calculated, σ V T h can be predicted for different gate lengths (Lg), cross-sections, and intrinsic variability parameters, without further simulations. The validity of the PBP model is demonstrated for three state-of-the-art architectures (FinFETs, nanowire FETs, and nanosheet FETs) with different Lg, cross-sections, and drain biases (VD). The relative errors between the predicted and simulated data are lower than 10%, in the 92% of the cases
PB IEEE
SN 2168-6734
YR 2022
FD 2022-10-17
LK https://hdl.handle.net/10347/45406
UL https://hdl.handle.net/10347/45406
LA eng
NO J. G. Fernandez, N. Seoane, E. Comesaña and A. García-Loureiro, "Pelgrom-Based Predictive Model to Estimate Metal Grain Granularity and Line Edge Roughness in Advanced Multigate MOSFETs," in IEEE Journal of the Electron Devices Society, vol. 10, pp. 953-959, 2022, doi: 10.1109/JEDS.2022.3214928
NO This work was supported by the Spanish MICINN, Xunta de Galicia, and FEDER Funds under Grant RYC-2017-23312, Grant PID2019-104834GB-I00, Grant ED431F 2020/008, and Grant ED431C 2022/16
DS Minerva
RD 30 abr 2026