Low-Temperature Poly-Si and Oxide (LTPO) thin-film transistors (TFTs) have been successfully manufactured and the AMOLED panels with LTPO TFTs are adapted to Apple Watch. The manufacturing technology of LTPO TFT such as stack-up structure, device characteristics, panel image quality with LRR driving, and degradation of reliability in oxide TFT is explained and discussed.

Amorphous InGaZnO 4 (a-IGZO) thin film transistors (TFTs) are promising devices in backplane technology. Since a-IGZO TFTs are very sensitive to the fabrication processes, they need stable process to keep their initial deposition properties. Herein we improved the stability of a-IGZO by applying N 2 O plasma. The stability characteristic of a-IGZO TFT was improved with N 2 O plasma. V th shift was 1.5V for 10,000s under NBTS with illumination which was the best result in the world.

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To accommodate emergencies involving the solitary aged, we have developed a collapse-sensing phone with a GPS receiving chipset and a CDMA sending chipset that reports the location of the individual to a local control center. A GIS has been developed to display the position of the caller on the map of a control system that enables administrative officers to rescue the aged people.

Authentication methods on smartphone are demanded to be implicit to users with minimum users' interaction. Existing authentication methods (e.g. PINs, passwords, visual patterns, etc.) are not effectively considering remembrance and privacy issues. Behavioral biometrics such as keystroke dynamics and gait biometrics can be acquired easily and implicitly by using integrated sensors on smartphone. We propose a biometric model involving keystroke dynamics for implicit authentication on smartphone. We first design a feature extraction method for keystroke dynamics. And then, we build a fusion model of keystroke dynamics and gait to improve the authentication performance of single behavioral biometric on smartphone. We operate the fusion at both feature extraction level and matching score level. Experiment using linear Support Vector Machines (SVM) classifier reveals that the best results are achieved with score fusion: a recognition rate approximately 97.86% under identification mode and an error rate approximately 1.11% under authentication mode.