Effective annealing and crystallization of si film for advanced TFT system
Abstract The effect of the crystallization and activated annealing of Si films using an excimer laser and the new CW blue laser are described and compared with furnace annealing for application in advanced TFTs and for future applications. Pulsed excimer laser annealing (ELA) is currently being used extensively as a low‐temperature poly‐silicon (LTPS) process on glass substrates as its efficiency is high in the ultra‐violet (UV) region for thin Si films with thickness of 40–60 nm. ELA enables extremely low resistivity relating to high crystallinity for both the n‐ and p‐type Si films. On the other hand, CW blue laser diode annealing (BLDA) enables the smooth Si surface to have arbitral crystal grains from micro‐grains to an anisotropic huge grain structure only by controlling its power density. Both annealing techniques are expected to be applied in the future advanced TFT systems.
High Mobility Thin Film Transistors Fabricated on a Plastic Substrate at a Processing Temperature of 110°C
We present results from excimer laser processed poly-silicon thin film transistors fabricated on a plastic substrate. Except for the temperature rise during excimer laser irradiation for crystallization, the highest processing temperature that the substrate was subjected to was 110°C. Transistor field-effect mobility of 250 cm2/V·s and sub-threshold swing of 0.16 V/decade were measured in these devices. These are the best characteristics reported to date for a thin film transistor (TFT) on a plastic substrate. The performance is attributed to our optimized laser crystallization process of helium sputtered Si films, laser-assisted low-temperature doping processes, and transient annealing.
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.