Using Ti-doped GaZnO/InGaZnO/Ti-doped GaZnO sandwich composite Structure to enhance the device performances of transparent thin-film transistors
In this study, two transparent thin-film transistors (TFTs) with distinct channel designs were fabricated. The first was a single channel TFT (SC-TFT) with a typical 50-nm-thick amorphous indium-gallium-zinc oxide (a-IGZO) layer, and the second was a sandwich composite-channel TFT (CC-TFT) comprising three layers of 10-/30-/10-nm-thick Ti-doped GaZnO (GTZO)/a-IGZO/GTZO. In the CC-TFT, the bottom GTZO thin film in the composite channel exhibited a highly smooth surface, serving as a buffer template for superior IGZO channel layer thin-film growth. The studies of material quality indicated that the CC-TFT exhibited improved a-IGZO thin-film quality; relative to the SC-TFT, the oxygen vacancy concentration of the IGZO layer in the composite structure was reduced from 25.1% to 18.2% because of the adoption of a GTZO buffer layer. The CC-TFT demonstrated a high level of device performance, exhibiting an excellent field-effect mobility of 14.1 cm2/V-s, subthreshold swing of 0.33 V/decade, off current of 2.92 × 10−12 A, threshold voltage of 1.7 V, and on-off current ratio of 3.95 × 107.
The effects of high-energy electron beam irradiation on the properties of IGZO thin films prepared by rf magnetron sputtering
S. H. Jung, H. J. Moon, M. K. Ryu, K. I. Cho, B. S. Bae and E.-J. Yun* College of New IT Engineering, Hoseo University, Asan, Chungnam 336-795, Korea Department of Semiconductor and Display Engineering, Hoseo University, Asan, Chungnam 336-795, Korea Laboratory Oxide Electronics Research Team, ETRI, Daejeon 305-350, Korea Department of Semiconductor and Display Engineering and College of New IT Engineering, Hoseo University, Asan, Chungnam 336-795, Korea Department of Semiconductor and Display Engineering and Department of System Control Engineering, Hoseo University, Asan, Chungnam 336-795, Korea
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.