2 resultados para HIGH-K DIELECTRIC

em Massachusetts Institute of Technology


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Scaling down of the CMOS technology requires thinner gate dielectric to maintain high performance. However, due to the depletion of poly-Si gate, it is difficult to reduce the gate thickness further especially for sub-65 nm CMOS generation. Fully silicidation metal gate (FUSI) is one of the most promising solutions. Furthermore, FUSI metal gate reduces gate-line sheet resistance, prevents boron penetration to channels, and has good process compatibility with high-k gate dielectric. Poly-SiGe gate technology is another solution because of its enhancement of boron activation and compatibility with the conventional CMOS process. Combination of these two technologies for the formation of fully germanosilicided metal gate makes the approach very attractive. In this paper, the deposition of undoped Poly-Si₁₋xGex (0 < x < 30% ) films onto SiO₂ in a low pressure chemical vapor deposition (LPCVD) system is described. Detailed growth conditions and the characterization of the grown films are presented.

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In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.