Preparation and Characterization ofHigh-k Aluminum Oxide Thin Films by Atomic Layer Deposition for Gate Dielectric Applications

Present work deals with the Preparation and characterization of high-k aluminum oxide thin films by atomic layer deposition for gate dielectric applications.The ever-increasing demand for functionality and speed for semiconductor applications requires enhanced performance, which is achieved by the continuous miniaturization of CMOS dimensions. Because of this miniaturization, several parameters, such as the dielectric thickness, come within reach of their physical limit. As the required oxide thickness approaches the sub- l nm range, SiO 2 become unsuitable as a gate dielectric because its limited physical thickness results in excessive leakage current through the gate stack, affecting the long-term reliability of the device. This leakage issue is solved in the 45 mn technology node by the integration of high-k based gate dielectrics, as their higher k-value allows a physically thicker layer while targeting the same capacitance and Equivalent Oxide Thickness (EOT). Moreover, Intel announced that Atomic Layer Deposition (ALD) would be applied to grow these materials on the Si substrate. ALD is based on the sequential use of self-limiting surface reactions of a metallic and oxidizing precursor. This self-limiting feature allows control of material growth and properties at the atomic level, which makes ALD well-suited for the deposition of highly uniform and conformal layers in CMOS devices, even if these have challenging 3D topologies with high aspect-ratios. ALD has currently acquired the status of state-of-the-art and most preferred deposition technique, for producing nano layers of various materials of technological importance. This technique can be adapted to different situations where precision in thickness and perfection in structures are required, especially in the microelectronic scenario.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Utilization Of Weeds And Agriwaste By Popularizing Handpapermaking In Kerala, India

Kerala in south India grows several cash crops such as banana and pineapple, the crop residues of which are sources of natural fibres that can be used in hand papermaking. Kerala, however, does not have a tradition in hand papermaking. The following is an account of an attempt to popularize the art and craft of hand papermaking among self-help groups as a means of self-employment and waste utilization, using fibres extracted from agriwaste and local plants