Abstract
As the technology nodes of semiconductor devices have become finer and more complex, progressive scaling down has been implemented to achieve higher densities for electronic devices. Thus, three-dimensional (3D) channel field-effect transistors (FETs), such as fin-shaped FETs (FinFETs) and gate-all-around FETs (GAAFETs), have become popular as they have increased effective surface areas for the channels (Weff), owing to the scaling down strategy. These 3D channel FETs, which have completely covered channel structures with gate oxide and metal, are prone to the self-heating effect (SHE). The SHE is generally known to degrade the on-state drain current; however, when AC pulsed inputs are applied to these devices, the SHE also degrades the off-state leakage current during the off-phase of the pulse. In this study, an optimization methodology to minimize leakage current generation by the SHE is examined.
Highlights
Research papers related to technology computer-aided design (TCAD) and machine learning (ML) have been actively published [1,2,3,4,5,6]
By using the neural network modeling method and a genetic algorithm, we propose a methodology for the optimization of the thermal properties of gate-all-around field-effect transistors (GAAFETs)
Since our targeted GAAFET is designed for 5 nm node technology, the linear constrained condition on the genetic algorithm—which limited the sum of Wchn and Hchn to 10 nm—was applied to optimize the device charateristics within the design space of technology node specification
Summary
Research papers related to technology computer-aided design (TCAD) and machine learning (ML) have been actively published [1,2,3,4,5,6]. Since our simulated GAAFET is targeted as having LP applications, we applied a pulsed signal as the input to the gate electrode to examine and optimize the leakage properties induced by the SHE, while the device commenced operation under the zero-biased state of the pulse. The zero-biased state indicates that there is no external heat generation through the device; CTH behaves as a heating source when the actual heating source, the self-heating effect, is removed due to the zero bias on the contact electrodes. It is the same principle with the example of natural response, where. By using the neural network modeling method and a genetic algorithm, we propose a methodology for the optimization of the thermal properties of GAAFETs
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