In this study, a high-performance AlGaN/GaN high electron mobility transistor (HEMT) is presented to improve its electrical operation by employing an inner field-plate (IFP) structure. Prior to the IFP structure analysis, we compared the measured and simulated direct current characteristics of the fabricated two-finger conventional T-shaped gate HEMTs. Then, the AlGaN/GaN HEMT with a drain-side field plate (FP) structure was suggested to enhance the breakdown voltage characteristics. The maximum breakdown voltage recorded with a 0.8 μm stretched FP structure was 669 V. Finally, the IFP structure was interfaced with the gate head of the device to compensate the radio frequency characteristics, choosing the optimum length of the drain-side FP structure. Compared to the 0.8 μm stretched FP structure, the IFP structure showed improved frequency characteristics with minimal difference to the breakdown voltage. The frequency variation caused by changing the passivation thickness was also analyzed, and the optimum thickness was identified. Thus, IFP AlGaN/GaN HEMT is a promising candidate for high-power and high-frequency applications.

In this study, we investigated the operational characteristics of AlGaN/GaN high electron mobility transistors (HEMTs) by applying the copper-filled trench and via structures for improved heat dissipation. Therefore, we used a basic T-gate HEMT device to construct the thermal structures. To identify the heat flow across the device structure, a thermal conductivity model and the heat transfer properties corresponding to the GaN, SiC, and Cu materials were applied. Initially, we simulated the direct current (DC) characteristics of a basic GaN on SiC HEMT to confirm the self-heating effect on AlGaN/GaN HEMT. Then, to verify the heat sink effect of the copper-filled thermal structures, we compared the DC characteristics such as the threshold voltage, transconductance, saturation current, and breakdown voltage. Finally, we estimated and compared the lattice temperature of a two-dimensional electron gas channel, the vertical lattice temperature near the drain-side gate head edge, and the transient thermal analysis for the copper-filled thermal trench and via structures. Through this study, we could optimize the operational characteristics of the device by applying an effective heat dissipation structure to the AlGaN/GaN HEMT.

This study investigates metal-insulator-semiconductor high electron mobility transistor DC characteristics with different gate dielectric layer compositions and thicknesses, and lattice temperature effects on gate leakage current by using a two-dimensional simulation. We first compared electrical properties, including threshold voltage, transconductance, and gate leakage current with the self-heating effect, by applying a single Si₃N₄ dielectric layer. We then employed different Al₂O₃ dielectric layer thicknesses on top of the Si₃N₄, and also investigated lattice temperature across a two-dimensional electron gas channel layer with various dielectric layer compositions to verify the thermal effect on gate leakage current. Gate leakage current was significantly reduced as the dielectric layer was added, and further decreased for a 15-nm thick Al₂O₃ on a 5-nm Si₃N₄ structure. Although the gate leakage current increased as Al₂O₃ thickness increased to 35 nm, the breakdown voltage was improved.

Combustible gases, such as CH₄ and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH₄ and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH₄ and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.