Models for Bridging Defects Test and Diagnosis - Models in Hardware Testing - page 56

with temperature. Thereby, at low temperatures the bridging resistance induces
higher I DDQ values. Furthermore, the probability of causing faulty logic behaviour
is also higher. Some works ( Kundu 1998 ; Schuermyer et al. 2004 ) give experi-
mental evidence of this phenomenon and show how testing results at two different
temperatures are useful to detect device outliers.
However, there are some drawbacks when introducing temperature in the pro-
duction testing environment. Techniques based on temperature variation are time
consuming and expensive, especially for low temperatures, which furthermore re-
quire specialised equipment.

2.4 Diagnosis of Bridging Defects

Diagnosis is the process which identifies the type of fault and locates the failure
site of a faulty device. Subsequently, failure analysis can be performed to physically
examine the defect. Precise diagnosis is important since it helps manufacturers to
solve process problems, improving yield and saving time on physical failure analy-
sis, which is time consuming and require significant investment in equipments, tools
and qualified personnel.
Diagnosis techniques combine simulation results with the data obtained from the
ATE (Automatic Test Equipment). Most of the techniques involve two main ele-
ments: a fault model and a comparison algorithm. Thus, using accurate fault models
is a key factor. If models are not accurate, the result may be an imprecise or even an
incorrect location of the failure site.
Fault diagnosis techniques can be broadly classified into two groups: cause-effect
and effect-cause techniques ( Abramovici et al. 1994 ). Cause-effect diagnosis tech-
niques are based on fault simulations to determine the possible response of a circuit
in the presence of faults. This information is compared with the response obtained
from the tester in order to obtain the fault location. Some cause-effect techniques use
a pre-computed fault dictionary, which is a database containing the faulty responses
of each fault. The algorithm then determines which fault from the dictionary best
matches the faulty behaviour observed on the tester. Techniques using a fault dic-
tionary are also known as static diagnosis techniques. However, with the increasing
complexity and number of transistors in today's ICs, sometimes it is not feasible to
build a dictionary for every possible fault, since the size of the dictionary would be
prohibitive. Thereby, a lot of effort is focused on reducing and compressing the size
of fault dictionaries ( Pomeranz and Reddy 1992 ; Boppana et al. 1996 ; Chess and
Larrabee 1999 ). Another possibility is using dynamic diagnosis techniques, which
analyse the response of the faulty circuit. The list of fault candidates is reduced
based on the response of the circuit and only the most probable faults are considered.
The effect-cause approach (Abramovici 1980) backtracks logic errors from the
primary outputs to the location of the fault deducing the internal values of the cir-
cuit. In principle, most of these diagnosis techniques do not require neither fault
dictionary nor fault enumeration.