The double-gate tunneling field-effect transistor (DGTFET) is investigated with two channel lengths (50 and 20 nm), along with the effect of the variation of the device design on its overall direct-current (DC) and radiofrequency (RF) performance. The studied design parameters are the drain doping abruptness (or spread) and its shift relative to the gate electrode. Additionally, the gate work function, and the dielectric material and its thickness are investigated. The studied performance parameters are the ON/OFF ratio, the maximum cutoff frequency, the subthreshold swing, and the ambipolar current. The device’s figure of merit (FOM) is expressed as a weighted-sum objective function for optimization by a genetic algorithm (GA). The results are validated against multifactorial experimentation to study the effect of changing each parameter on the FOM. It is shown that the genetic optimization can enhance the performance of the 20-nm-channel device to become comparable to that of a device with a long channel of 50 nm. The GA is run multiple times using parallel processing, MATLAB, and a technology computer-aided design (TCAD) model to validate its efficiency for the optimization of electronic devices when a TCAD model can be built without a great need to define a mathematical model in closed form.

中文翻译：

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遗传算法优化隧穿场效应晶体管的性能

研究了双沟道隧穿场效应晶体管（DGTFET）的两个沟道长度（50和20 nm），以及器件设计变化对其总体直流（DC）和射频（RF）的影响性能。研究的设计参数是漏极掺杂突变（或扩散）及其相对于栅电极的偏移。此外，还研究了栅极功函数，介电材料及其厚度。研究的性能参数为开/关比，最大截止频率，亚阈值摆幅和双极性电流。设备的品质因数（FOM）表示为加权和目标函数，可通过遗传算法（GA）进行优化。通过多因素实验验证了结果，以研究更改每个参数对FOM的影响。结果表明，遗传优化可以增强20 nm通道设备的性能，使其与具有50 nm长通道的设备的性能可比。使用并行处理，MATLAB和技术计算机辅助设计（TCAD）模型可多次运行GA，以在无需大量定义数学模型的情况下构建TCAD模型时验证其在优化电子设备方面的效率。封闭表格。