Nanotechnology Now

Our NanoNews Digest Sponsors

Heifer International

Wikipedia Affiliate Button

Home > News > IMEC transferred Design-for-Manufacturing tool for embedded SRAMs to Samsung Electronics

April 21st, 2009

IMEC transferred Design-for-Manufacturing tool for embedded SRAMs to Samsung Electronics

Abstract:
IMEC successfully transferred MemoryVAM (Memory Variability Aware Modeling), the first EDA tool for statistical memory analysis, to Samsung Electronics. The tool predicts yield loss of SRAMs caused by the process variations of deep-submicron IC technologies.

Story:
IMEC's MemoryVAM is an essential tool to avoid already at design time the most likely reasons for failure, anticipating and correcting weak design spots before tape-out, and hence avoiding redesign spins after processing. The tool also provides key help to memory and system designers to estimate yield loss due to changes of for example cycle time, access time and power consumption (static/dynamic) caused by process variations.



"With MemoryVAM IMEC completes a missing steppingstone in industrial and academic state-of-the-art Design-For-Manufacturing flows which lacked such modeling capabilities for memories;" said Rudy Lauwereins, Vice President Smart Systems Technology Office at IMEC. "This is especially interesting for embedded SRAMs, which are considered to be the most sensitive component to process variations of today's systems-on-chip. We are excited that the tool is now being successfully used by the product engineering design teams at Samsung Electronics."



"With collaboration with IMEC, a new novel statistical analysis tool MemoryVAM has become available in our embedded SRAM design." said Kyu-Myung Choi, Vice President of Design Technology Team at Samsung Electronics. "We expect that MemoryVAM will be helpful for parametric yield modeling of embedded SRAM design and for understanding the unknown gap between design and silicon results due to process variability in deep sub-micron technology below 45nm."



MemoryVAM is part of IMEC's Variability Aware Modeling (VAM) flow which is the first holistic flow capable of percolating process variations all the way from the process technology up to the System on a Chip (SoC) level. VAM enables to track the reasons for yield loss and the relative likelihood of such failure. Unlike most of the statistical analysis techniques, VAM is unique in its kind by accurately keeping track of all statistical process, design and environmental correlations tightly linked together and across abstraction levels.



MemoryVAM builds on IMEC's revolutionary method to analyze performance metrics of semiconductor memories under process variations. The method requires mainly three input items. The first is a transistor level netlist description of a segment of the memory describing all circuitry involved from input to output. The second one is a set of parameters describing the internal architecture of the memory, thus how the memory is built from the segment information, including redundancy and error correction code infrastructure. The third one is information about the variability of the devices and interconnects used in the underlying technology. This information can be provided in either the form of statistical distributions of certain transistor parameters, scattered data obtained via statistical simulation of the device or just plain data set obtained via silicon measurements.



The power of MemoryVAM lies in the analysis of parameters of the memory that can be directly embedded in the input netlist by the designer. These are then used to carry out the implementation of the method, without requiring additional custom modeling steps from the user. The key to this strategy is the ability to complement the analysis of a nominal memory model under test with statistically sampled variants of the devices. This is done by using an in-house developed statistically enhanced Monte Carlo technique, although it also allows the usage of any other available enhanced sampling technique. With this novel and fast analytical technique, statistical information on the critical path percolates to the complete SRAM organization level, resulting in a realistic prediction of the yield as perceived by the memory tester and/or equivalent BIST (built-in-self-testing) technique.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related News Press

News and information

A nanoscale wireless communication system via plasmonic antennas: Greater control affords 'in-plane' transmission of waves at or near visible light August 27th, 2016

Forces of nature: Interview with microscopy innovators Gerd Binnig and Christoph Gerber August 26th, 2016

A promising route to the scalable production of highly crystalline graphene films August 26th, 2016

Graphene under pressure August 26th, 2016

Chip Technology

A nanoscale wireless communication system via plasmonic antennas: Greater control affords 'in-plane' transmission of waves at or near visible light August 27th, 2016

A promising route to the scalable production of highly crystalline graphene films August 26th, 2016

Analog DNA circuit does math in a test tube: DNA computers could one day be programmed to diagnose and treat disease August 25th, 2016

Silicon nanoparticles trained to juggle light: Research findings prove the capabilities of silicon nanoparticles for flexible data processing in optical communication systems August 25th, 2016

Announcements

A nanoscale wireless communication system via plasmonic antennas: Greater control affords 'in-plane' transmission of waves at or near visible light August 27th, 2016

Forces of nature: Interview with microscopy innovators Gerd Binnig and Christoph Gerber August 26th, 2016

A promising route to the scalable production of highly crystalline graphene films August 26th, 2016

Graphene under pressure August 26th, 2016

Tools

Nanofiber scaffolds demonstrate new features in the behavior of stem and cancer cells August 25th, 2016

50 years after the release of the film 'Fantastic Voyage,' science upstages fiction: Science upstages fiction with nanorobotic agents designed to travel in the human body to treat cancer August 25th, 2016

University of Puerto Rico and NASA back in the news XEI reports August 23rd, 2016

Spider silk: Mother Nature's bio-superlens August 22nd, 2016

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoTech-Transfer
University Technology Transfer & Patents
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project







Car Brands
Buy website traffic