Nanotechnology Now

Our NanoNews Digest Sponsors





Heifer International

Wikipedia Affiliate Button


android tablet pc

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

Industrial Nanotech, Inc. to Publish PCAOB Audited Financials July 31st, 2014

Nanostructured metal-oxide catalyst efficiently converts CO2 to methanol: Highly reactive sites at interface of 2 nanoscale components could help overcome hurdle of using CO2 as a starting point in producing useful products July 31st, 2014

Carnegie Mellon Chemists Create Nanofibers Using Unprecedented New Method July 31st, 2014

Pressure probing potential photoelectronic manufacturing compound July 31st, 2014

Chip Technology

Pressure probing potential photoelectronic manufacturing compound July 31st, 2014

Nanometrics Reports Second Quarter 2014 Financial Results July 30th, 2014

A*STAR and industry form S$200M semiconductor R&D July 25th, 2014

A Crystal Wedding in the Nanocosmos July 23rd, 2014

Announcements

Industrial Nanotech, Inc. to Publish PCAOB Audited Financials July 31st, 2014

Nanostructured metal-oxide catalyst efficiently converts CO2 to methanol: Highly reactive sites at interface of 2 nanoscale components could help overcome hurdle of using CO2 as a starting point in producing useful products July 31st, 2014

Carnegie Mellon Chemists Create Nanofibers Using Unprecedented New Method July 31st, 2014

Pressure probing potential photoelectronic manufacturing compound July 31st, 2014

Tools

Carnegie Mellon Chemists Create Nanofibers Using Unprecedented New Method July 31st, 2014

New Objective Focusing Nanopositioner from nPoint July 30th, 2014

University of Manchester selects Anasys AFM-IR for coatings and corrosion research July 30th, 2014

Analytical solutions from Malvern Instruments support University of Wisconsin-Milwaukee researchers in understanding environmental effects of nanomaterials July 30th, 2014

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







© Copyright 1999-2014 7th Wave, Inc. All Rights Reserved PRIVACY POLICY :: CONTACT US :: STATS :: SITE MAP :: ADVERTISE