CH’NG , MING ZONG
(2017)
Fsm-Based Enhanced March C- Algorithm For Memory Built-In Self-Test.
Masters thesis, Universiti Sains Malaysia.
Abstract
Algorithms plays an important role in the Memory Built-In Self-Test as its structure will define the fault coverage of the system. Thus, the improvement of the algorithms will allow more fault types being identified in single test. For MARCH C- algorithm, it was a quite balance algorithm as it has the ability to cover Address Decoder Fault, Stuck-At Fault, Transition Fault and Coupling Fault but it was not able to identify Read Destructive Fault and Data Retention Fault. To increase the fault coverage of the MARCH C- algorithm on that two fault types, some enhancements were needed to make on the algorithm. Through the analysis of the fault patterns and other algorithm, it was found that multiple read operation within a single MARCH element from MARCH-NU algorithm can aid in the identification of the Read Destructive Fault whereas having HOLD time in the MARCH 9N algorithm would expose the Data Retention Fault. By integrating both new fault identification methodologies into the MARCH C- algorithm and enhance it so that it would able to increase the identification of the Data Retention Fault and Read Destructive Fault by 100% and without causing any performance to interfere with the original fault identification ability. Besides that, the enhanced MARCH C- algorithm can structurally identify the fault types and differentiate among the behavioural faults (Data Retention Faults) and common memory faults as well as among Stuck-At Fault and Transition Fault. Fault injection test were carried out to ensure the coverage of the enhanced MARCH C- algorithm. It was having high passing rate which are 100%
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identification of the Stuck-At Fault, Transition Fault, Data Retention Fault, Inversion Read Fault, Incorrect Read Fault and Read Destructive Fault. It also obtained 95.16% for the State Coupling Fault and Idempotent Coupling Fault. As a conclusion, the enhanced MARCH C- algorithm had increased its fault type identification in Data Retention Fault and Read Destructive Fault while having the ability to structurally identify the fault types.
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