Difference between revisions of "Dyer2011"
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|abstract=<p>Accurate characterization of spatial variation is essential for statistical performance analysis and modeling, post-silicon tuning, and yield analysis. Existing approaches for spatial modeling either assume that: (i) non-stationarities exist due to a smoothly varying trend component or that (ii) the process is stationary within regions associated with a predefined grid. While such assumptions may hold when profiling certain classes of variations, many studies now suggest that spatial variability is likely to be highly non-stationary. In order to provide spatial models for non-stationary process variations, we introduce a new hybrid spatial modeling framework that models the spatially varying random field as a union of non-overlapping rectangular regions where the process is assumed to be locallystationary. To estimate the parameters in our hybrid spatial model, we introduce a host of techniques for efficient detection of regions over which the process variations are locally-stationary. We verify our models and results on measurements collected from 65nm FPGAs.</p> | |abstract=<p>Accurate characterization of spatial variation is essential for statistical performance analysis and modeling, post-silicon tuning, and yield analysis. Existing approaches for spatial modeling either assume that: (i) non-stationarities exist due to a smoothly varying trend component or that (ii) the process is stationary within regions associated with a predefined grid. While such assumptions may hold when profiling certain classes of variations, many studies now suggest that spatial variability is likely to be highly non-stationary. In order to provide spatial models for non-stationary process variations, we introduce a new hybrid spatial modeling framework that models the spatially varying random field as a union of non-overlapping rectangular regions where the process is assumed to be locallystationary. To estimate the parameters in our hybrid spatial model, we introduce a host of techniques for efficient detection of regions over which the process variations are locally-stationary. We verify our models and results on measurements collected from 65nm FPGAs.</p> | ||
|pages=in press | |pages=in press | ||
|month=6 | |||
|year=2011 | |||
|booktitle=Design Automation Conference (DAC) | |booktitle=Design Automation Conference (DAC) | ||
|title=Hybrid Spatial Modeling of Non-Stationary Process Variations | |title=Hybrid Spatial Modeling of Non-Stationary Process Variations | ||
|entry=inproceedings | |entry=inproceedings | ||
}} | }} | ||
Revision as of 03:38, 4 September 2021
| Dyer2011 | |
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| entry | inproceedings |
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| author | Eva Dyer and Mehrdad Majzoobi and Farinaz Koushanfar |
| booktitle | Design Automation Conference (DAC) |
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| month | 6 |
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| pages | in press |
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| title | Hybrid Spatial Modeling of Non-Stationary Process Variations |
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| year | 2011 |
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| url | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5981935 |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093