Prediction of Statistical Distribution on Nanosheet FET by Geometrical Variability Using Various Machine Learning Models

• Published in: IEEE access Vol. 11; pp. 125217 - 125225
• Main Authors: Ha, Jonghyeon, Kim, Sun Jin, Bang, Minji, Lee, Gyeongyeop, Suh, Minki, Shim, Minseob, Kim, Chong-Eun, Kim, Jungsik
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: CAD; Computational modeling; Computer aided analysis; Computer aided design; Decision trees; Design automation; Diameters; Electric variables; Field effect transistors; Logic gates; Machine learning; Nanoscale devices; Nanosheet field-effect transistor; Nanosheets; prediction; Predictive models; Probability distribution; Semiconductor devices; Simulation; Statistical analysis; Statistical distributions; technology computer-aided design simulation; Thickness; Threshold voltage; Variability; Wires
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3330773

Due to the aggressive scaling down of logic semiconductors, the difficulty of semiconductor component processes has increased. As the structure of components becomes more complex, the time and cost of processes and simulations have risen. Machine learning is now being used to analyze the electrical characteristics data of semiconductor components and apply the trained machine learning to next-generation semiconductor development. Machine learning trained on process data and simulation results can quickly and accurately predict which electrical characteristics change significantly when the component's structure changes and which parameters have a significant impact on the electrical characteristic changes. This paper presents suitable machine learning models for analyzing and predicting the electrical characteristics (on-current (<inline-formula> <tex-math notation="LaTeX">I_{on} </tex-math></inline-formula>), off-current (<inline-formula> <tex-math notation="LaTeX">I_{off} </tex-math></inline-formula>), threshold voltage (<inline-formula> <tex-math notation="LaTeX">V_{th} </tex-math></inline-formula>), subthreshold swing (SS), and drain induced barrier lowering (DIBL)) and statistical distribution (mean and standard deviation of the electrical characteristics) resulting from geometrical variability (sheet thickness (<inline-formula> <tex-math notation="LaTeX">T_{wire} </tex-math></inline-formula>), sheet diameter (<inline-formula> <tex-math notation="LaTeX">D_{wire} </tex-math></inline-formula>), oxide thickness (<inline-formula> <tex-math notation="LaTeX">T_{ox} </tex-math></inline-formula>), gate length (<inline-formula> <tex-math notation="LaTeX">L_{g} </tex-math></inline-formula>), spacer length (<inline-formula> <tex-math notation="LaTeX">L_{sp} </tex-math></inline-formula>), gate metal work-function (WF)) in nanosheet field-effect transistor (NSFET), which are a next-generation logic device. Machine learning models, including regulation-based models (Ridge and LASSO) and tree-based models (decision tree (DT), random forest (RF), extreme gradient boost (XGBoost), and light gradient boost machine (LGBM)), are trained on technology computer-aided design (TCAD) simulation data. The LGBM more accurately predicts the electrical characteristics and statistical distribution of the NSFET than the other models. Additionally, we analyze the effect of geometrical variability on the NSFET based on feature importance.