Diagnosis technique plays a key role during the rapid development of the semiconductor memories, for catching the design and manufacturing failures and improving the overall yield and quality. Investigation on efficient diagnosis algorithms is very important due to the expensive and complex fault/failure analysis process. We propose March-based RAM diagnosis algorithms which not only locate faulty cells but also identify their types. The diagnosis complexity is O(17N) and O((17 + 10B)N) for bit-oriented and word-oriented diagnosis algorithms, respectively, where N represents the address number and B is the data width. Using the proposed algorithms, stuck-at faults, state coupling faults, idempotent coupling faults and inversion coupling faults can be distinguished. Furthermore, the coupled and coupling cells can be located in the memory array. Our word-oriented diagnosis algorithm can distinguish all of the inter-word and intra-word coupling faults, and locate the coupling cells of the intra-word inversion and idempotent coupling faults. With additional 2B - 1 operations, the algorithm can further locate the intra-word state coupling faults. With improved diagnostic resolution and test time, the proposed algorithms facilitate the development and manufacturing of semiconductor memories.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Applied Mathematics