DocumentCode :
703950
Title :
A scan partitioning algorithm for reducing capture power of delay-fault LBIST
Author :
Nan Li ; Dubrova, Elena ; Carlsson, Gunnar
Author_Institution :
R. Inst. of Technol., Stockholm, Sweden
fYear :
2015
fDate :
9-13 March 2015
Firstpage :
842
Lastpage :
847
Abstract :
It is well-known that high power consumption in test mode can cause problems such as overheating and IR-drop which have negative effect on circuit reliability and yield. The problem is particularly hard in the case of at-speed delay-fault testing where it cannot be mitigated by lowering the clock frequency. The difficulty increases even further if pseudo-random rather than ATPG patterns are used for testing. ATPG patterns can be chosen selectively, as well as re-ordered and specified in a power-friendly manner. Pseudo-random test patterns are much harder to control. In this paper, we present a scan partitioning algorithm for reducing capture power targeting delay-fault LBIST. The algorithm uses a novel weighted S-graph model in which the weights are determined by signal probability analysis. Our experimental results show that, on average, the presented method reduces average capture power by 50% and peak capture power by 39% with less than 2% loss in the transition fault coverage.
Keywords :
automatic test pattern generation; built-in self test; circuit reliability; fault simulation; graph theory; logic testing; probability; ATPG patterns; IR-drop; S-graph model; at-speed delay-fault testing; automatic test pattern generator; capture power targeting delay-fault LBIST; circuit reliability; clock frequency; logic built in self test; overheating; power-friendly manner; pseudorandom test patterns; scan partitioning algorithm; signal probability analysis; Automatic test pattern generation; Built-in self-test; Circuit faults; Logic gates; Partitioning algorithms; Power demand;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015
Conference_Location :
Grenoble
Print_ISBN :
978-3-9815-3704-8
Type :
conf
Filename :
7092502
Link To Document :
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