A machine learning approach to accelerate reliability prediction in nanowire FETs from self-heating perspective

Abstract

Nanowire Field Effect Transistors (NWFETs) have been considered as the next-generation technology for sub-10 nm technology nodes, succeeding FinFETs. However, the highly confined nature of Nanowire FETs creates reliability issues that significantly impact their performance. Therefore, this work proposes a machine learning-based technique for analyzing the self-heating-induced reliability issues in NWFETs. The influence of self-heating effects in NWFET has been predicted in terms of saturation current (I_dsat), threshold voltage (V_th), the maximum carrier temperature along the channel (eTmax), and the maximum Lattice temperature (LTmax) with multivariable regression. TCAD-assisted machine learning has been used for algorithm training and prediction. A dataset has been created by varying the parameters of the NWFETs like the thickness of the channel (t_si), the thickness of oxide (t_ox), Length of source/drain (L_sd), length of source/drain contact (L_sdc), doping concentrations etc. The Random Forest Regression algorithm has been used to estimate the performance of NWFETs in predicting the desired output parameters suitably with the given dataset.