Accepted for publication in IEEE Transactions on CAD ICAS Failing Vector Identification Based on Overlapping Intervals of Test Vectors in a Scan BIST Environment Chunsheng Liu and Krishnendu Chakrabarty Department of Electrical Computer Engineering Duke University 130 Hudson Hall Box 90291 Durham NC 27708 Contact author Krishnendu Chakrabarty Phone 919 660 5244 Fax 919 660 5293 Email krish ee duke edu ABSTRACT We present a new scan BIST approach for determining failing vectors for fault diagnosis This approach is based on the application of overlapping intervals of test vectors to the circuit under test and it is especially suitable for faults that are detected by a relatively small number of pseudorandom test patterns Two MISRs are used in an interleaved fashion to generate intermediate signatures thereby obviating the need for multiple test sessions The knowledge of failing and fault free intervals is used to obtain a set S of candidate failing vectors that includes all the actual true failing vectors We propose a signature analysis method based on overlapping sections and the principle of superposition to effectively prune the candidate set We present analytical results to determine an appropriate interval length and the degree of overlap as well as upper and lower bounds on the size of S We also determine a lower bound on the number of true failing vectors through a simple graph model Finally we present experimental results for the ISCAS 89 benchmark circuits to demonstrate the effectiveness of the proposed scan BIST diagnosis approach Keywords Candidate vector set diagnostic resolution fault diagnosis signature analysis superposition This research was supported in part by the National Science Foundation under grants CCR 9875324 and CCR 0204077 1 1 Introduction As process technologies shrink and designs become more complex built in self test BIST is gaining increasing acceptance as an industry wide test solution 1 In particular the combination of scan design and BIST commonly referred to as scan BIST is now especially common 2 Scan BIST techniques typically apply a large number of patterns from a pseudorandom pattern generator PRPG to the circuit under test CUT via scan chains The test responses are then captured by the scan chain and a compact signature is generated using a multiple input signature register MISR see Figure 1 However a problem with this approach is that the signature provided by the MISR does not contain enough diagnostic information either to identify failing vectors or to precisely identify error capturing scan cells The pass fail information obtained from the MISR at the end of the test session is usually insufficient to diagnose the failure via effectcause analysis Fault diagnosis is essential for the identification of manufacturing defects and for yield learning The cost of diagnosis is proportional to the time required for failure analysis which can be extremely high for a scan BIST scheme involving tens of thousands or millions of vectors 3 Therefore there is a pressing need for BIST schemes that provide adequate diagnostic information without burdening the failure analysis process with superfluous information The diagnostic information in scan BIST can be classified as space information and time information respectively The former refers to the set of scan cells that capture errors during the BIST session This problem has received a lot of attention recently and a number of methods involving scan chain partitioning with multiple test sessions have been proposed for precisely identifying the failing scan cells 4 7 A more difficult problem in scan BIST diagnosis is that of identifying the set of failing vectors This is because the length of a scan chain in a typical BIST scheme is usually much smaller than the number of test vectors applied to the CUT As a result fewer practical techniques are available today for rapidly identifying a small set of candidate failing vectors Early work on failing vector identification was based on the analysis of LFSR sequences 8 the use of cycling registers 9 and error correcting codes 10 11 An alternative approach that does not require intermediate signatures was presented in 12 These techniques suffer from the drawback of limitations on error multiplicity 8 diagnostic aliasing 9 10 11 and high overhead 10 12 Recently a method based on the combination of cycling registers and pruning techniques was proposed for failing vector identification 13 While this approach is useful in narrowing down the set of candidate failing vectors it suffers from the 2 CUT Scan Chains MISR PRPG Test Responses Test Patterns Figure 1 Generic scan BIST scheme drawback that it does not identify all the failing vectors In this paper we present a technique for failing vector identification based on the use of overlapping intervals of test vectors and the principle of superposition This approach is especially suitable for diagnosing faults that are detected by a small number of pseudorandom patterns i e have relatively low detection probability An interval is a set of consecutive test vectors An advantage of this approach is that all failing vectors are included in a reduced set of candidate vectors for failure analysis Unlike the approach based on test windows 18 where test vectors are partitioned into non overlapping segments our method is based on overlapping intervals of test vectors In combination with a novel pruning procedure the overlap allows us to prune the set of candidate failing vectors more efficiently An interval that does not contain a failing vector can be omitted from the set of candidate failing vectors The overlap ensures that if a failing interval I1 is followed by a non failing interval I2 only the set difference I1 I2 needs to be retained in the set of candidate failing vectors As an enhancement to the basic interval method proposed in 19 we show here that interval overlap also allows us to use a simple yet efficient signature analysis technique based on superposition that can significantly prune the candidate set This pruning method does not require any additional information In order to reduce the candidate set further post processing procedures can be performed as optional steps 19 which require additional signatures The proposed interval based approach also allows us to determine upper and lower bounds on the number of candidate failing vectors as well as a lower bound on the number of actual true failing vectors The