To increase the radio-frequency (RF) performance of AlGaN/GaN-based fin-type high electron mobility transistors (HEMTs), a novel T-gate process was developed and applied to fabricate a device with high RF performance. In a single lithography process, the applied T-gate process shows a technique for forming a T-gate using the reactivity difference of several photoresists. The fabricated device has a steep fin width (Wfin) of 130 nm, a fin height (Hfin) of 250 nm, and a gate length (LG) of 190 nm. The device exhibits a low leakage current (Ioff) of 6.6 × 10−10 A/mm and a high Ion/Ioff current ratio of 4.7 × 108. Moreover, the fabricated device achieved a high cut-off frequency (fT) of 9.7 GHz and a very high maximum oscillation frequency (fmax) of 27.8 GHz. The fmax value of the proposed device is 138% higher than that of GaN-based fin-type HEMTs without T-gate.

In this work, a capacitorless one-transistor dynamic random access memory (1T-DRAM) based on a polycrystalline silicon (poly-Si) metal-oxide-semiconductor field-effect transistor was designed and analyzed through a technology computer-aided design (TCAD) simulation. A poly-Si thin film was utilized within the device because of several advantages, including its low fabrication cost and the feasibility of its use in high-density three-dimensional (3D) memory arrays. An asymmetric dual-gate structure is proposed to perform the write “1” operation and achieve high retention characteristics. The proposed 1T-DRAM cell demonstrates a high sensing margin of 8.73 μA/μm and a high retention time of 704.4 ms compared to previously reported 1T-DRAMs, even at a high temperature. In addition, the effect of grain boundaries on the memory performance of the proposed device was investigated, and the results validated the excellent reliability of its retention characteristics even in the presence of grain boundaries (>64 ms at T = 358 K).

This paper presents a one-transistor dynamic random-access memory (1T-DRAM) cell based on a gate-all-around junction-less field-effect transistor (GAA-JLFET) with a Si/SiGe heterostructure for high-density memory applications. The proposed 1T-DRAM achieves the sensing margin using the difference in hole density in the body region between ‘1’ and ‘0’ states. The Si/SiGe heterostructure forms a quantum well in the body and reduces the band-to-band tunneling (BTBT) barrier between the body and drain. Compared with the performances of the 1T-DRAM with Si homo-structure, the proposed 1T-DRAM improves the sensing margin and retention time because its storage ability is enhanced by the quantum well. In addition, the thin BTBT barrier reduced the bias condition for the program operation. The proposed 1T-DRAM showed a high potential for memory applications by obtaining a high read current ratio at ‘1’ and ‘0’ states about 10⁸ and a long retention time above 10 ms.

In this study, a capacitorless one-transistor dynamic random-access memory (1T-DRAM) based on a polycrystalline silicon nanotube structure with a grain boundary (GB) is designed and analyzed using technology computer-aided design (TCAD) simulation. The proposed 1T-DRAM has the improved electrical performances because the outer gate (OG) and the inner gate (IG) effectively control the charges in the channel and body regions. IG has an asymmetric structure with an underlap ( $L_{\mathrm {underlap}}$ ) region to reduce the Shockley–Read–Hall (SRH) recombination rate. In the proposed 1T-DRAM, the write “1” operation is performed by band-to-band tunneling between the OG and the IG. The proposed 1T-DRAM cell exhibited a sensing margin of $422 ~\mu \text{A}/\mu \text{m}$ and a retention time of 120 ms at $T$ = 358 K.

In this paper, Artificial Neural Networks (ANNs) are applied to the prediction of lateral behavior of single and group piles. The results of neural networks are compared with the measured data from the model tests. A series of model tests were performed with single and group piles in the Nak-Dong River Sand. For the verification of applicability of BPNN (Back Propagation Neural Network), the Modified BPNN and SNN (Sequential Neural Network), a total of 200 model test results for single and group piles were used. Also, in this study, the structure of neural network with one input layer-two hidden layerone output layer was used. The number of neuron for each hidden layer determined to be 30 from the results of model test and the learning rate determined to be 0.9 to optimize network learning. Compared with BPNN, the learning epoch of the modified BPNN for the same learning pattern is reduced to 88% at the maximum and the convergence to a global minimum is guaranteed. The investigation confirmed that a SNN with feedback is more effective than a conventional ANN without feedback to simulate the lateral behavior for single and group piles. It is concluded from this study that artificial neural ne tworks based on pile-soil interaction models can be developed by properly predicting and learning algorithms based on a comprehensive data set, and that useful inferences can be made from such models.