Magdy Abadir
ABSTRACT
As manufacturing technologies continue to scale, it has become more difficult for modeling and simulation to accurately predict silicon timing behavior. This "unpredictability" can lead to low yield or it can be corrected by over-design, both are not desirable. To optimize a design process, one needs a set of models and tools that reflect the reality. In this talk, we take timing analysis as an example to illustrate a framework that aims to achieve this "design for reality" objective. This novel methodology utilize data mining techniques to identify the most relevant design features that impact the predictability of timing data. Experimental results based on industrial designs and silicon data will be presented to explain the data learning techniques their applications.
Index Terms
- Design for reality: knowledge discovery in design and test data
Recommendations
Data mining in design and test processes: basic principles and promises
ISPD '13: Proceedings of the 2013 ACM International symposium on Physical DesignThis talk discusses several application examples to illustrate the basic principles of applying data mining in design and test. Two types of data mining are seen in most of the applications: novelty detection and feature-based rule learning. The ...
On effective IDDQ Testing of low-voltage CMOS circuits using leakage control techniques
The use of low-threshold devices in low0voltage CMOS circuits leads to an exponential increase in the intrinsic leakage current. This threatens the effectiveness of IDDQ testing for such low-voltage circuits because it is difficult to differentiate a ...
Comparing the Impact of Power Supply Voltage on CMOS- and FinFET-Based SRAMs in the Presence of Resistive Defects
AbstractCMOS technology scaling has reached its limit at the 22 nm technology node due to several factors including Process Variations (PV), increased leakage current, Random Dopant Fluctuation (RDF), and mainly the Short-Channel Effect (SCE). In order to ...
Comments