Prediction of Process Variation Effect for Ultrascaled GAA Vertical FET Devices Using a Machine Learning Approach

• Published in: IEEE transactions on electron devices Vol. 66; no. 10; pp. 4474 - 4477
• Main Authors: Ko, Kyul, Lee, Jang Kyu, Kang, Myounggon, Jeon, Jongwook, Shin, Hyungcheol
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Artificial intelligence; Artificial neural network (ANN); Artificial neural networks; Computation; Computational modeling; Computer simulation; Cost analysis; Data models; Field effect transistors; Gallium arsenide; gate-all-around (GAA); Learning theory; Logic gates; Machine learning; machine learning (ML); Predictions; Process parameters; process variation (PV); Semiconductor devices; Solid modeling; vertical device
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2019.2937786

In this brief, we present an accurate and efficient machine learning (ML) approach which predicts variations in key electrical parameters using process variations (PVs) from ultrascaled gate-all-around (GAA) vertical FET (VFET) devices. The 3-D stochastic TCAD simulation is the most powerful tool for analyzing PVs, but for ultrascaled devices, the computation cost is too high because this method requires simultaneous analysis of various factors. The proposed ML approach is a new method which predicts the effects of the variability sources of ultrascaled devices. It also shows the same degree of accuracy, as well as improved efficiency compared to a 3-D stochastic TCAD simulation. An artificial neural network (ANN)-based ML algorithm can make multi-input -multi-output (MIMO) predictions very effectively and uses an internal algorithm structure that is improved relative to existing techniques to capture the effects of PVs accurately. This algorithm incurs approximately 16% of the computation cost by predicting the effects of process variability sources with less than 1% error compared to a 3-D stochastic TCAD simulation.