A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

The sensitivity of semiconductor devices to any microscopic perturbation is increasing with the continuous shrinking of device technology. Even the small fluctuations have become more acute for highly scaled nano-devices. Therefore, these fluctuations need to be addressed extensively in order to continue further device scaling. In this paper, we mainly focus on three sources of intrinsic parameter fluctuation including the work function fluctuation (WKF), the random dopant fluctuation (RDF), and the interface trap fluctuation (ITF) for gate-all-around (GAA) silicon (Si) nanosheet (NS) MOSFETs. Generally, the effect of these fluctuations is analyzed using a time-consuming device simulation process. A machine learning (ML) based powerful and efficient artificial neural network (ANN) model is used to accelerate this process. Firstly, the effects of fluctuation sources are analyzed individually by using the ANN model and results have been presented that show the WKF variations dominate the variation of threshold voltage, off-state current, and on-state current among other fluctuation sources. Next, we examine the combined effect of three fluctuation sources. It is crucial because considering only one fluctuation can result in unexpected variations due to other fluctuations appearing in the device. Consequently, the ANN model is used to estimate the combined effects as well. The results show that the proposed model predicts the outputs with an R-2-score of 99% and an error rate of less than 1%. In addition, the ML is also utilized to calculate the permutation importance of input variables as a measure to investigate the contribution of each fluctuation source.