March-based diagnosis algorithm for static random-access memory stuck-at faults and transition faults

The fast growing of technologies has enabled the Static Random Access Memories (SRAMs) to contain higher density of cell array which make it prone to be affected by defects. This factor contributes to challenges faced in the area of memory testing. Functional test is a standard testing procedure to determine the functionality of the memory by performing a sequence of read write operations to detect faults. March tests are widely used during functional testing due to its higher fault coverage and less time complexity. March tests are developed based on functional fault model (FFM) such as stuck-at faults (SAFs), transition faults (TFs) and coupling faults (CFs). Recent March tests have incorporate a diagnostic capability that enables faults not only to be detected but also distinguished. The diagnosis capability of March tests are verified based on generated fault syndromes dictionary that correspond to the detection of targeted faults during read operations. However, existing March tests with diagnostic capability were not able to distinguish between SAFs and TFs. Therefore, this thesis proposed a new two phase algorithm based on March tests to specifically distinguish the two faults. The fault syndrome based on read operations of the proposed algorithm is generated to determine its diagnostic capability. The SAFs and TFs are able to be distinguished from each other when different syndromes are generated for each fault. The fault syndromes are then validated by simulating the sequence of propose algorithms on defect free test circuit and test circuit that contains defects mapped to SAFs and TFs. The test circuit consists of write circuitry, six transistors (6T) SRAM cell and sense amplifier circuit. The simulation also includes the testing of MATS++ and March C- algorithms in order to verify the circuit functionality and validate the types of defects belong to both SAFs and TFs. Findings shows that the propose algorithm generates different fault syndromes for SAFs and TFs hence make them distinguishable. Results obtained from simulation validate the generated fault syndromes thus confirmed the ability of this algorithm to detect and distinguish between SAFs and TFs.

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