Indium selenide (InSe), a promising two-dimensional (2D) material, exhibits various advantageous properties, including low effective mass and near-infrared exciton emission.However, its high surface-to-volume ratio necessitates effective encapsulation to enhance stability and performance.This study examined the impact of poly(methyl methacrylate) (PMMA) encapsulation on the properties of InSe.Raman spectroscopy revealed a blue shift in the vibrational modes of InSe upon PMMA encapsulation, suggesting strain and doping effects.Electrical measurements of InSe-based field-effect transistors indicated a reduction in on-current and a positive shift in threshold voltage, consistent with p-doping and decreased carrier mobility.These findings provide insights into the interaction between 2D materials and encapsulation polymers, emphasizing the role of PMMA in modifying the electrical characteristics of InSe devices.This study contributes to the development of encapsulation strategies to improve the performance and reliability of 2D semiconductor devices.

This paper proposes an equipment intelligence study that builds an integrated database system to collect equipment and sensor data generated during real-time processes, which are in turn used to diagnose abnormalities in semiconductor manufacturing equipment. The integrated database system leverages edge computing to convert data collected simultaneously from equipment and sensors into standardized communication protocols on a single central server. The constructed system not only detects equipment abnormalities in advance, but also helps identify the exact cause of these abnormalities by collecting optical emission spectroscopy sensor data generated from the equipment, such as voltage, pressure, and gas flow generated during the SiO 2 deposition process. The system analyzes the results by determining meaningful data from the collected data and feeding them into a fault detection and classification algorithm. This research is promising for greatly improving the efficiency and yield of the overall continuous semiconductor manufacturing process.

A key aspect in understanding silicon etching mechanisms is analyzing the characteristics of volatile reaction products, whose properties, particularly stoichiometry, vary significantly with temperature and fluorine coverage.These variations influence crucial etching attributes, such as reaction kinetics and the transition from isotropic to anisotropic etching.Temperature and fluorine coverage thus emerge as the primary determinants of the stoichiometry and complexity of reaction products.At low temperatures, the Langmuir-Hinshelwood mechanism dominates, where bimolecular reactions between adsorbed species produce SiF 4 as the major product.As temperature increases, thermal desorption becomes the primary mechanism, favoring the formation of SiF 2 and SiF.Similarly, fluorine coverage influences reaction pathways: under low coverage, SiF 4 is predominant, while higher coverage leads to more complex intermediates such as Si 2 F 6 and Si 3 F 8 through bimolecular or trimolecular reactions involving SiF 3 and SiF 2 .

Dohyun Kim, Min Choi, Tae Yeon Kim, Il-Gyu Choi, Sung-Jae Chang, Hyun-Wook Jung, Ho-Kyun Ahn, and Hyun Seok Lee. Applied Science and Convergence Technology 2024;33:148-51. https://doi.org/10.5757/ASCT.2024.33.5.148

Perovskite halide materials exhibit excellent optoelectronic properties, such as tunable bandgaps, high optical absorption coefficients, and long carrier diffusion lengths.These characteristics have enabled perovskite solar cells to achieve power conversion efficiencies (PCEs) of over 26 %.However, organometallic halide perovskites are sensitive to ambient conditions; moreover, moisture affects their stability and efficiency.In this study, perovskite films were fabricated via additive engineering under ambient air conditions.This enhanced the moisture stability of the perovskite precursors, enabling the fabrication of uniform perovskite films under humid conditions.Moreover, devices fabricated with additives exhibited higher average efficiencies compared to pristine devices, achieving the best PCE of 19.00 % at relative humidity of 50 % with NH 4 Cl additive.These results demonstrate the effectiveness of additives in producing efficient cells in ambient air, which can facilitate the mass production of perovskite solar cells.

Ovonic threshold switch (OTS) selectors are promising devices to suppress leakage current of emerging memories in a cross-point architecture.In this study, enhanced OTS selector performance by nitrogen doping on amorphous SiTe chalcogenide was analyzed by focusing on the subthreshold region with different temperatures.The parameters of trap states including effective trap density, inter-trap distance, and activation energy are known to govern the subthreshold behaviors of amorphous chalcogenides.The parameters derived from a trap limited Poole-Frenkel based conduction model and Arrhenius plot closely correlate with the nitrogen doping effect on SiTe.Increased inter-trap distance, lowered trap density, and raised activation energy by N doping hamper electron hopping through trap states, thereby suppressing the leakage current.

Hexamethyldisilazane (HMDS) is a cost-effective and stable compound used to synthesize thin films for applications such as silicon nitride thin-film deposition. Plasma-enhanced chemical vapor deposition is commonly used to deposit thin films from HMDS because plasma offers sufficient energy to effectively decompose HMDS and facilitates the formation of films. Various gases are added to the plasma to modulate its characteristics. This study investigated the effect of N 2 addition to atmospheric-pressure Ar/HMDS plasma. Optical emission spectroscopy measurements provided valuable insights into the characteristics of the plasma under different N 2 gas flow rates, and the importance of N 2 as a deposition parameter was discussed. The introduction of N 2 in the Ar/HMDS plasma decreased the excitation, vibrational, and rotational temperatures, significantly changing the composition of reactive species in the plasma.

As a radio frequency (RF) power and plasma process monitoring sensor, a voltage current (VI) probe has been utilized and developed to monitor and optimize plasma processes in semiconductor fabrication. However, many components are needed to deliver the RF power from the power supply to the plasma electrodes such as an RF connector and RF cable which affect the RF power transmission efficiency. Therefore, it is necessary to analyze the characteristics of RF power transmission lines with respect to the external parameter configuration using a VI probe. In this study, we investigated the characteristics of RF signals using a VI probe for various RF power transmission lines, including the VI probe installation position, RF connector type, and RF cable length. The RF signals were measured as a function of the input power in a lowpressure argon discharge. Consequently, the RF power loss was significant when long RF cables were used for the transmission line. Analyzing the characteristics of RF transmission lines using a VI probe can improve the reliability of RF measurement properties for plasma processing applications.

This research proposes an innovative method for optimizing plasma etching processes in semiconductor manufacturing using machine learning (ML). Plasma etching is a critical process in defining precise patterns on semiconductor materials, requiring accurate process control. In this study, we employ the ML model based on big data to develop a predictive model that can capture complex relationships between process variables and plasma etching outcomes as the thickness of MoS 2 . The ML model demonstrated high accuracy, closely aligning with actual experimental results. The experiments confirmed uniform etching across the entire 4-inch wafer, with a precision of approximately 1 nm. Based on this research, we aim to apply ML prediction models to various process conditions of plasma etching and gain deeper insight into the ML's capabilities for two-dimensional materials in semiconductor manufacturing.