Woo S, Jeon J, Kim S. Prediction of Device Characteristics of
Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning.
Micromachines (Basel) 2023;
14:504. [PMID:
36984910 PMCID:
PMC10051704 DOI:
10.3390/mi14030504]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/12/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current-voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.
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