Earticle

A Gate-All-Around (GAA) structure for 3D DRAM was fabricated using an ALD-based oxide semiconductor channel to enable low-temperature processing and suppress leakage current. Spacer engineering with ALD and dry-oxidized SiO2 effectively reduced leakage paths compared to PECVD. The use of Al2O3 as the gate insulator further improved insulation. The device demonstrated promising performance with Ion = 1.30 μA and SS = 113 mV/decade at VDS = 1.1 V, indicating its potential for next-generation memory applications.

In this study, we significantly enhanced the optical performance and stability of IGZO/PbS QD phototransistors by introducing Ga2O3 passivation layer. The resulting IGZO/PbS QD/Ga₂O₃ demonstrated a responsivity of 196.69 A/W and a detectivity of 5.47 × 10¹² Jones under 1550 nm illumination.

To overcome limitations of binary computing systems, such as interconnect delays and power inefficiency, we demonstrate CMOS-compatible ternary logic devices based on TeOx/IGTO p-n heterojunction TFTs. Fabricated at 150 °C, these devices exhibit stable negative differential transconductance (NDT) with a peak-to-valley ratio over 103. By monolithically integrating them with p-channel TeOx TFTs, we realize ternary inverters capable of producing a distinct intermediate logic state through optimized transconductance alignment. This lowtemperature process supports scalable, energy-efficient logic integration, highlighting oxide semiconductors as a strong platform for next generation multi-valued computing.

Atomic layer deposition (ALD) process optimization of Al2O3-doped ZnO (AZO) interlayer (IL) inserted between channel and electrodes was held to improve electrical contact properties of amorphous In-Ga-Zn-O (a-IGZO) thin film transistors (TFTs). In particular, the modulation of the ALD supercycle duty and variations in Al₂O₃ injection cycles were investigated and optimized for their effects on contact properties during the deposition of the AZO IL. The use of alloy-AZO2 IL effectively improves overall device performance.

Monolithic three-dimensional (M3D) integration enhances chip performance and area efficiency by vertically stacking memory elements over CMOS logic layers. [1] Low-temperature processing is essential in M3D to prevent thermal degradation of the underlying circuitry. Oxide semiconductors (OS), particularly indium oxide (In2O3), have shown promise due to their high carrier mobility, low leakage current, and stability under lowtemperature fabrication processes. [2] However, oxygen vacancies (VO) in In2O3 can act as trap states, leading to instability under positive bias temperature stress (PBTS). This study explores the impact of in-situ plasma surface treatment on the In2O3 channel layer of top-gate thin-film transistors (TFTs) before gate insulator deposition. The results demonstrate that plasma surface treatment significantly improves electrical performance and operational stability by reducing interface traps, thereby enhancing device reliability and reducing variability. These findings provide valuable insights for the development of high-performance oxide semiconductor devices for M3D applications.

A method to control subthreshold swing (SS) in transistors by engineering Sn concentration through atomic layer deposition (ALD) subcycles was demonstrated. By increasing the number of Sn ALD cycles, the channel trap density (Nₜ) increased, resulting in SS modulation within the range of 0.161 to 0.231 V/decade. While SS increased, mobility improved, and threshold voltage (VTH) remained stable, indicating that the device's performance was not compromised. Sn concentration adjustment via ALD provides a more efficient approach for SS control, offering advantages in power efficiency and integration, particularly for AMOLED and OLED applications.

Post-Traumatic Stress Disorder (PTSD) is a serious psychiatric condition that requires objective and accurate diagnosis for effective early intervention. In this study, we propose a deep learning-based computer-aided diagnosis (CAD) system using event-related potentials (ERP) to distinguish individuals with PTSD from healthy controls. We introduce a novel model, TIME-CNN (Temporal Integration with Multi-scale dEcoding CNN), designed to extract temporal features through multi-scale depthwise convolutions and residual connections. EEG data were collected during an auditory oddball task from 51 PTSD patients and 39 matched healthy controls. As a result, the proposed TIME-CNN outperformed its shallower counterpart (Shallow TIME-CNN) in both classification accuracy (86.05% vs. 76.40%) and training time (7.5 vs. 12.5 hours). These findings demonstrate the effectiveness and practicality of the TIME-CNN model for ERP-based PTSD diagnosis.