Models for Bridging Defects Test and Diagnosis - Models in Hardware Testing - page 59

resistance explaining the faulty behaviour, and therefore, this candidate can be re-
moved from the list. In a similar way, Khursheed et al. ( 2009 ) alsopresenteda
methodology where resistive intervals were used to diagnose resistive bridges. How-
ever, in this work resistive intervals at different power supply values are used to
improve the accuracy of the diagnosis procedure.
Instead of using the pre-computed information stored in a table used by fault dic-
tionaries, the fault simulation procedures Wu and Rudnick ( 1999 , 2000 ) consist in
comparing the actual output response of the failing device to the expected response
for each possible bridge. A list of fault candidates is then generated. Faults whose
effects most closely match the response of the failing device are identified as can-
didates. The advantage of this approach compared to fault dictionaries is that fault
simulation is faster. In the work developed by Wu and Rudnick ( 2000 ), information
from single SA faults is used. Single SA fault simulations are performed during fault
diagnosis for a more accurate result.
All the methods discussed above are implemented at inter-gate level. However,
bridging faults at intra-gate level are also possible. The work by Fan et al. ( 2006 )ad-
dresses the logic diagnosis of intra-gate bridging faults by means of a transformation
method.

2.4.2 Current Diagnosis Techniques

The quiescent current-based techniques for the diagnosis of bridging defects are
reviewed in this subsection. The quiescent current flowing through the defect is
analyzed in terms of diagnosis purposes. The impact of the consumption generated
at the downstream gates is also analyzed.

2.4.2.1 I DDQ -Based Diagnosis

I DDQ has demonstrated to be also effective for the diagnosis of bridging faults
although, at the beginning, it was believed that I DDQ could not provide enough
information for diagnosis purposes ( Acken and Millman 1992 ). Subsequently, dif-
ferent works demonstrated the effectiveness of I DDQ for bridging fault localisation.
The main advantage of current methodologies is that fault signatures are easy to
generate. The first works ( Aitken 1991 , 1992 ; Chakravarty and Suresh 1994 ; Nigh
et al. 1997 ) were based on the simple I DDQ bridging fault model, which assumes
that abnormal high current is generated when the bridged nets are set to different
logic values. Aitken ( 1991 ) demonstrated that combining logic and current informa-
tion diagnosis resolution was improved. Subsequently, the same author presented
diagnosis results without using logic information ( Aitken 1992 ). Chakravarty and
Suresh ( 1994 ) proposed an I DDQ based diagnosis algorithm which considers also
whether one of the nodes involved in the bridge is internal or not. Subsequently,
Nigh et al. ( 1997 ) relied on a set of realistic bridges based on layout information