Nonvolatile memory functionality of ZnO nanowire transistors controlled by mobile protons.

We demonstrated the nonvolatile memory functionality of ZnO nanowire field effect transistors (FETs) using mobile protons that are generated by high-pressure hydrogen annealing (HPHA) at relatively low temperature (400 °C). These ZnO nanowire devices exhibited reproducible hysteresis, reversible switching, and nonvolatile memory behaviors in comparison with those of the conventional FET devices. We show that the memory characteristics are attributed to the movement of protons between the Si/SiO(2) interface and the SiO(2)/ZnO nanowire interface by the applied gate electric field. The memory mechanism is explained in terms of the tuning of interface properties, such as effective electric field, surface charge density, and surface barrier potential due to the movement of protons in the SiO(2) layer, consistent with the UV photoresponse characteristics of nanowire memory devices. Our study will further provide a useful route of creating memory functionality and incorporating proton-based storage elements onto a modified CMOS platform for FET memory devices using nanomaterials.

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.