Statistical Variation

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Jun Feng - One of the best experts on this subject based on the ideXlab platform.

  • CAD/Graphics - Segmenting deformable soft-body meshes based on Statistical Variation information for piecewise Active Shape Model
    2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009
    Co-Authors: Peng Du, H.s. Ip Horace, Jun Feng
    Abstract:

    This paper proposes an algorithm for segmenting deforming soft-body meshes based on Statistical Variation information extracted from the deforming meshes. The Variation information is extracted by performing a global principal component analysis (PCA) on the set of meshes. Eigen-Variation Similarity (EVS) and Eigen-Variation Magnitude (EVM) are then defined for the vertices and triangle faces of the meshes based on the extracted Variation information. A multiple-source region growing algorithm is presented for segmenting a mesh that favors grouping faces with similar Variations into a same component. We apply the proposed mesh segmentation algorithm to the construction of piecewise Active Shape Model (ASM) and use such piecewise ASM to reconstruct unseen meshes. Experimental results show that our algorithm outperforms several state-of-the-art methods in terms of reconstruction accuracy.

  • Segmenting deformable soft-body meshes based on Statistical Variation information for piecewise Active Shape Model
    2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009
    Co-Authors: Peng Du, H.s. Ip Horace, Jun Feng
    Abstract:

    This paper proposes an algorithm for segmenting deforming soft-body meshes based on Statistical Variation information extracted from the deforming meshes. The Variation information is extracted by performing a global principal component analysis (PCA) on the set of meshes. eigen-Variation similarity (EVS) and eigen-Variation magnitude (EVM) are then defined for the vertices and triangle faces of the meshes based on the extracted Variation information. A multiple-source region growing algorithm is presented for segmenting a mesh that favors grouping faces with similar Variations into a same component. We apply the proposed mesh segmentation algorithm to the construction of piecewise active shape model (ASM) and use such piecewise ASM to reconstruct unseen meshes. Experimental results show that our algorithm outperforms several state-of-the-art methods in terms of reconstruction accuracy.

Peng Du - One of the best experts on this subject based on the ideXlab platform.

  • CAD/Graphics - Segmenting deformable soft-body meshes based on Statistical Variation information for piecewise Active Shape Model
    2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009
    Co-Authors: Peng Du, H.s. Ip Horace, Jun Feng
    Abstract:

    This paper proposes an algorithm for segmenting deforming soft-body meshes based on Statistical Variation information extracted from the deforming meshes. The Variation information is extracted by performing a global principal component analysis (PCA) on the set of meshes. Eigen-Variation Similarity (EVS) and Eigen-Variation Magnitude (EVM) are then defined for the vertices and triangle faces of the meshes based on the extracted Variation information. A multiple-source region growing algorithm is presented for segmenting a mesh that favors grouping faces with similar Variations into a same component. We apply the proposed mesh segmentation algorithm to the construction of piecewise Active Shape Model (ASM) and use such piecewise ASM to reconstruct unseen meshes. Experimental results show that our algorithm outperforms several state-of-the-art methods in terms of reconstruction accuracy.

  • Segmenting deformable soft-body meshes based on Statistical Variation information for piecewise Active Shape Model
    2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009
    Co-Authors: Peng Du, H.s. Ip Horace, Jun Feng
    Abstract:

    This paper proposes an algorithm for segmenting deforming soft-body meshes based on Statistical Variation information extracted from the deforming meshes. The Variation information is extracted by performing a global principal component analysis (PCA) on the set of meshes. eigen-Variation similarity (EVS) and eigen-Variation magnitude (EVM) are then defined for the vertices and triangle faces of the meshes based on the extracted Variation information. A multiple-source region growing algorithm is presented for segmenting a mesh that favors grouping faces with similar Variations into a same component. We apply the proposed mesh segmentation algorithm to the construction of piecewise active shape model (ASM) and use such piecewise ASM to reconstruct unseen meshes. Experimental results show that our algorithm outperforms several state-of-the-art methods in terms of reconstruction accuracy.

John Sudijono - One of the best experts on this subject based on the ideXlab platform.

  • Impact of NBTI Induced Statistical Variation to SRAM Cell Stability
    2006 IEEE International Reliability Physics Symposium Proceedings, 2006
    Co-Authors: Giuseppe La Rosa, Wee Loon Ng, Stewart Rauch, Robert Wong, John Sudijono
    Abstract:

    This work investigates the impact of negative bias temperature instability (NBTI) on the SRAM cell stability. As proposed by C. Wang et al., the stability of an SRAM cell can be determined by the peak current (ICRIT) of the "N curve". In our experiments a typical NBTI stress was applied to one of the two pull up transistors part of an SRAM cell designed by using an advanced submicron CMOS technology. Both the mean and variance of the pMOSFET threshold voltage shift in saturation (DeltaVtSAT) and the corresponding values of the ICRIT shifts (DeltaICRIT) were measured. An experimental correlation between the means and the variances of both parameters shifts was established and found consistent with the predicted simulated values in the case of ICRIT is degrading by only NBTI aging of the one or both pull up transistors. These results allow us to observe the direct impact of the NBTI shift of a pMOSFET transistor in a SRAM cell and the corresponding reduction to the static noise margin. In addition we propose, for the first time, a methodology to define a pMOSFET device NBTI target directly related to the SRAM cell stability and its dependence on SRAM design and the adopted CMOS technology. It is found that a more appropriate SRAM stability sensitive pMOSFET NBTI Vt SAT target cannot be limited to the VtSAT mean shift, but needs as well a quantification of the allowed variance and initial SRAM ICRIT distribution

S.s. Li - One of the best experts on this subject based on the ideXlab platform.

  • Comparison of Statistical Variation of threshold voltage in bulk and SOI MOSFETs
    Solid-state Electronics, 1992
    Co-Authors: Hung-sheng Chen, S.s. Li
    Abstract:

    Abstract The Statistical Variation of the threshold voltage induced by random distributed device parameters is examined in both bulk and SOI MOSFETs. Our study reveals that the threshold voltage of thin-film SOI MOSFETs is less sensitive to inherent fluctuations in device parameters. Design considerations in minimizing the Statistical threshold voltage Variation in SOI MOSFETs are discussed. By proper choice of the gate material, film thickness and channel doping density, enhancement of the production yield can be expected for high-performance SOI VLSI circuits.

  • A comparison of Statistical Variation of threshold voltage in bulk silicon and SOI MOSFETs
    1991 IEEE International SOI Conference Proceedings, 1991
    Co-Authors: Hung-sheng Chen, S.s. Li
    Abstract:

    Using previously developed models for the bulk and SOI (silicon-on-insulator) MOSFETs, the authors compare and analyze the Statistical Variation of the threshold voltage V/sub th/ in these MOSFETs with respect to Variation of device parameters such as doping density, oxide thickness, and channel length. The Statistical Variation of the threshold voltage reduction Delta V/sub th/ for three values of channel length is shown. The Statistical distribution is broadened with a pronounced asymmetry toward the higher value of Delta V/sub th/. It is also noted that the Statistical distribution in the SOI MOSFET is narrower than that of the bulk MOSFET. The optimized design region of a fully depleted SOI MOSFET with a threshold voltage distribution smaller than its bulk silicon counterpart is shown.

Muhammad A. Alam - One of the best experts on this subject based on the ideXlab platform.

  • Estimation of Statistical Variation in temporal NBTI degradation and its impact on lifetime circuit performance
    2007 IEEE ACM International Conference on Computer-Aided Design, 2007
    Co-Authors: Kunhyuk Kang, Sang Phill Park, Muhammad A. Alam
    Abstract:

    Negative bias temperature instability (NBTI) in MOSFETs is one of the major reliability concerns in sub-100 nm technologies. So far, studies of NBTI and its impact on circuit performance have assumed an average behavior of the degradation process. However, in very short channel devices, finite number of Si-H bonds in the channel can induce a Statistical random Variation of the degradation process. This results in significant random Vt Variations in PMOS transistor. The NBTI induced Variation depends on operating temperature and the effective stress period for the specific device. In this paper, we analyze the impact of stochastic temporal NBTI Variations and propose a compact circuit level Vt model. Using the proposed model, we show how temporal Vt Variations can affect the lifetime performance of different circuit topologies including 6T SRAM cell and random combinational logic circuits.