DocumentCode :
2703354
Title :
Data learning techniques and methodology for Fmax prediction
Author :
Chen, Janine ; Wang, Li.-C. ; Chang, Po-Hsien ; Zeng, Jing ; Yu, Stanley ; Mateja, Michael
Author_Institution :
Dept. of ECE, UC-Santa Barbara, Santa Barbara, CA, USA
fYear :
2009
fDate :
1-6 Nov. 2009
Firstpage :
1
Lastpage :
10
Abstract :
The question of whether or not structural test measurements can be used to predict functional or system Fmax, has been studied for many years. This paper presents a data learning approach to study the question. Given Fmax values and structural delay measurements on a set of sample chips, we propose a method called conformity check whose goal is to select a subset of conformal samples such that a more reliable predictor can be built on. Our predictor consists of two models, a conformal model that decides on a given chip if its Fmax is predictable or not, and a prediction model that outputs the predicted Fmax based on results obtained from structural test measurements. We explain the data learning methodology and study various data learning techniques using frequency data collected on a high-performance microprocessor design.
Keywords :
automatic test equipment; learning (artificial intelligence); microprocessor chips; network synthesis; Fmax prediction; conformity check; data learning techniques; microprocessor design; structural delay measurements; structural test measurements; Built-in self-test; Delay; Frequency conversion; Frequency measurement; Logic testing; Microprocessors; Predictive models; Semiconductor device measurement; Semiconductor device testing; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Test Conference, 2009. ITC 2009. International
Conference_Location :
Austin, TX
Print_ISBN :
978-1-4244-4868-5
Electronic_ISBN :
978-1-4244-4867-8
Type :
conf
DOI :
10.1109/TEST.2009.5355620
Filename :
5355620
Link To Document :
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