Journal ArticleDOI
Statistical Compact Model Extraction: A Neural Network Approach
TLDR
ANNs can model a much higher degree of nonlinearity compared to existing quadratic polynomial models and, hence, can even be used in sub-100-nm technologies to model leakage current that exponentially depends on process parameters.Abstract:
A technique for extracting statistical compact model parameters using artificial neural networks (ANNs) is proposed. ANNs can model a much higher degree of nonlinearity compared to existing quadratic polynomial models and, hence, can even be used in sub-100-nm technologies to model leakage current that exponentially depends on process parameters. Existing techniques cannot be extended to handle such exponential functions. Additionally, ANNs can handle multiple input multiple output relations very effectively. The concept applied to CMOS devices improves the efficiency and accuracy of model extraction. Results from the ANN match the ones obtained from SPICE simulators within 1%.read more
Citations
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Journal ArticleDOI
Machine learning model for predicting threshold voltage by taper angle variation and word line position in 3D NAND Flash memory
Journal ArticleDOI
Statistical compact model extraction for skew-normal distributions
TL;DR: A technique to extract statistical model parameters for skewed Gaussian process variations is proposed and results show that the extracted parameters, when simulated, match the performance parameter targets to within 3% for both Gaussian and skewed process variations.
Journal ArticleDOI
Parasitic $RC$ Aware Delay Corner Model for Sub-10-nm Logic Circuit Design
TL;DR: The delay variations predicted by the proposed model match well with the Monte Carlo simulation results at various simulation conditions, unlike the conventional corner model, which introduces pessimism.
Book ChapterDOI
Statistical Compact Model Extraction for Skewed Gaussian Variations
TL;DR: A technique for extracting Statistical Compact Model parameters for skewed Gaussian parameters is proposed by setting up a skewed back propagation of variance (SBPV) algorithm and analytical expressions for the statistics of the skewedGaussian process and performance parameters are derived.
Proceedings ArticleDOI
About IEEE Bangalore Section
TL;DR: In this paper , the authors summarized activities and initiatives taken by IEEE Bangalore section during the year 2012, and presented a survey of IEEE Bangalore Section activities and their initiatives for the year 2013.
References
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Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Proceedings ArticleDOI
Full-chip analysis of leakage power under process variations, including spatial correlations
TL;DR: The proposed method for analyzing the leakage current, and hence the leakage power, of a circuit under process parameter variations that can include spatial correlations due to intra-chip variation is presented.
Journal ArticleDOI
Statistical Compact Model Parameter Extraction by Direct Fitting to Variations
K. Takeuchi,Masami Hane +1 more
TL;DR: In this article, a statistical compact model parameter extraction method is proposed and described in detail, where the target of fitting is not the individual transistor, but statistically analyzed results (more specifically, principal components) of measured data.
Journal ArticleDOI
Analytical yield prediction considering leakage/performance correlation
TL;DR: A new chip-level statistical method to estimate the total leakage current in the presence of within-die and die-to-die variability is presented and an integrated approach to accurately estimate the yield loss when both frequency and power limits are imposed on a design is presented.
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