Post-CMOS wafer level growth of carbon nanotubes for low-cost microsensors--a proof of concept.

Here we demonstrate a novel technique to grow carbon nanotubes (CNTs) on addressable localized areas, at wafer level, on a fully processed CMOS substrate. The CNTs were grown using tungsten micro-heaters (local growth technique) at elevated temperature on wafer scale by connecting adjacent micro-heaters through metal tracks in the scribe lane. The electrical and optical characterization show that the CNTs are identical and reproducible. We believe this wafer level integration of CNTs with CMOS circuitry enables the low-cost mass production of CNT sensors, such as chemical sensors.

Correlation between density of states distributions and the fermi level position in interfacial regions of amorphous silicon thin film transistors

This paper investigates the variation of the integrated density of states with conduction activation energy in hydrogenated amorphous silicon thin film transistors. Results are given for two different gate insulator layers, PECVD silicon oxide and thermally grown silicon dioxide. The different gate insulators produce transistors with very different initial transfer characteristics, but the variation of integrated density of states with conduction activation energy is shown to be similar.

Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilizes a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subject's native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.