Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis
The impact of process variation in deep-submicron technologies is especially pronounced for SRAM architectures which must meet demands for higher density and higher performance at increased levels of integration. Due to the complex structure of SRAM, estimating the effect of process variation accura...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/63115 https://orcid.org/0000-0002-5977-2748 https://orcid.org/0000-0003-0737-3259 |
| _version_ | 1826215649487093760 |
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| author | Qazi, Masood Tikeka, Mehul Dolecek, Lara Shah, Devavrat Chandrakasan, Anantha P. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Qazi, Masood Tikeka, Mehul Dolecek, Lara Shah, Devavrat Chandrakasan, Anantha P. |
| author_sort | Qazi, Masood |
| collection | MIT |
| description | The impact of process variation in deep-submicron technologies is especially pronounced for SRAM architectures which must meet demands for higher density and higher performance at increased levels of integration. Due to the complex structure of SRAM, estimating the effect of process variation accurately has become very challenging. In this paper, we address this challenge in the context of estimating SRAM timing variation. Specifically, we introduce a method called loop flattening that demonstrates how the evaluation of the timing statistics in the complex, highly structured circuit can be reduced to that of a single chain of component circuits. To then very quickly evaluate the timing delay of a single chain, we employ a statistical method based on importance sampling augmented with targeted, high-dimensional, spherical sampling. Overall, our methodology provides an accurate estimation with 650X or greater speed-up over the nominal Monte Carlo approach. |
| first_indexed | 2024-09-23T16:37:16Z |
| format | Article |
| id | mit-1721.1/63115 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:37:16Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/631152022-09-29T20:22:57Z Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis Qazi, Masood Tikeka, Mehul Dolecek, Lara Shah, Devavrat Chandrakasan, Anantha P. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Microsystems Technology Laboratories Shah, Devavrat Shah, Devavrat Qazi, Masood Chandrakasan, Anantha P. The impact of process variation in deep-submicron technologies is especially pronounced for SRAM architectures which must meet demands for higher density and higher performance at increased levels of integration. Due to the complex structure of SRAM, estimating the effect of process variation accurately has become very challenging. In this paper, we address this challenge in the context of estimating SRAM timing variation. Specifically, we introduce a method called loop flattening that demonstrates how the evaluation of the timing statistics in the complex, highly structured circuit can be reduced to that of a single chain of component circuits. To then very quickly evaluate the timing delay of a single chain, we employ a statistical method based on importance sampling augmented with targeted, high-dimensional, spherical sampling. Overall, our methodology provides an accurate estimation with 650X or greater speed-up over the nominal Monte Carlo approach. 2011-05-25T18:11:41Z 2011-05-25T18:11:41Z 2010-04 2010-03 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-7054-9 1530-1591 INSPEC Accession Number: 11283197 http://hdl.handle.net/1721.1/63115 Qazi, M. et al. “Loop Flattening & Spherical Sampling: Highly Efficient Model Reduction Techniques for SRAM Yield Analysis.” Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010 : 801-806. © 2010 IEEE. https://orcid.org/0000-0002-5977-2748 https://orcid.org/0000-0003-0737-3259 en_US http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5456940&tag=1 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Qazi, Masood Tikeka, Mehul Dolecek, Lara Shah, Devavrat Chandrakasan, Anantha P. Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title | Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title_full | Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title_fullStr | Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title_full_unstemmed | Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title_short | Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis |
| title_sort | loop flattening spherical sampling highly efficient model reduction techniques for sram yield analysis |
| url | http://hdl.handle.net/1721.1/63115 https://orcid.org/0000-0002-5977-2748 https://orcid.org/0000-0003-0737-3259 |
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